publications

List of publications and preprints by Tom Hanika

2023

• 
  Stumme, G., D{{ü}}rrschnabel, D., Hanika, T.: Towards Ordinal Data Science, https://doi.org/10.48550/arXiv.2307.09477, (2023).

  URLBibTeXEndNoteDOIBibSonomy

  @misc{DBLP:journals/corr/abs-2307-09477,
  author = {Stumme, Gerd and D{{ü}}rrschnabel, Dominik and Hanika, Tom},
  journal = {CoRR},
  keywords = {publist},
  title = {Towards Ordinal Data Science},
  volume = {abs/2307.09477},
  year = 2023
  }
  

  %0 Generic
  %1 DBLP:journals/corr/abs-2307-09477
  %A Stumme, Gerd
  %A D{{ü}}rrschnabel, Dominik
  %A Hanika, Tom
  %D 2023
  %J CoRR
  %R 10.48550/arXiv.2307.09477
  %T Towards Ordinal Data Science
  %U https://doi.org/10.48550/arXiv.2307.09477
  %V abs/2307.09477
  

• 
  Hanika, T., Hirth, J.: Conceptual views on tree ensemble classifiers International Journal of Approximate Reasoning. 159, 108930 (2023).

  AbstractURLBibTeXEndNoteDOIBibSonomy

  Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.

  @article{HANIKA2023108930,
  abstract = {Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.},
  author = {Hanika, Tom and Hirth, Johannes},
  journal = {International Journal of Approximate Reasoning},
  keywords = {xai},
  pages = 108930,
  title = {Conceptual views on tree ensemble classifiers},
  volume = 159,
  year = 2023
  }
  

  %0 Journal Article
  %1 HANIKA2023108930
  %A Hanika, Tom
  %A Hirth, Johannes
  %D 2023
  %J International Journal of Approximate Reasoning
  %P 108930
  %R https://doi.org/10.1016/j.ijar.2023.108930
  %T Conceptual views on tree ensemble classifiers
  %U https://www.sciencedirect.com/science/article/pii/S0888613X23000610
  %V 159
  %X Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.
  

• 
  Stubbemann, M., Hille, T., Hanika, T.: Selecting Features by their Resilience to the Curse of Dimensionality (2023).

  BibTeXEndNoteBibSonomy

  @article{stubbemann2023selecting,
  author = {Stubbemann, Maximilian and Hille, Tobias and Hanika, Tom},
  keywords = {selecting},
  title = {Selecting Features by their Resilience to the Curse of Dimensionality},
  year = 2023
  }
  

  %0 Journal Article
  %1 stubbemann2023selecting
  %A Stubbemann, Maximilian
  %A Hille, Tobias
  %A Hanika, Tom
  %D 2023
  %T Selecting Features by their Resilience to the Curse of Dimensionality
  

• 
  Hirth, J., Horn, V., Stumme, G., Hanika, T.: Ordinal Motifs in Lattices, http://arxiv.org/abs/2304.04827, (2023).

  AbstractURLBibTeXEndNoteDOIBibSonomy

  Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.

  @misc{hirth2023ordinal,
  abstract = {Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.},
  author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom},
  keywords = {publist},
  title = {Ordinal Motifs in Lattices},
  year = 2023
  }
  

  %0 Generic
  %1 hirth2023ordinal
  %A Hirth, Johannes
  %A Horn, Viktoria
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2023
  %R 10.48550/arXiv.2304.04827
  %T Ordinal Motifs in Lattices
  %U http://arxiv.org/abs/2304.04827
  %X Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.
  

• 
  Stubbemann, M., Hanika, T., Schneider, F.M.: Intrinsic Dimension for Large-Scale Geometric Learning Transactions on Machine Learning Research. (2023).

  AbstractURLBibTeXEndNoteBibSonomy

  The concept of dimension is essential to grasp the complexity of data. A naive approach to determine the dimension of a dataset is based on the number of attributes. More sophisticated methods derive a notion of intrinsic dimension (ID) that employs more complex feature functions, e.g., distances between data points. Yet, many of these approaches are based on empirical observations, cannot cope with the geometric character of contemporary datasets, and do lack an axiomatic foundation. A different approach was proposed by V. Pestov, who links the intrinsic dimension axiomatically to the mathematical concentration of measure phenomenon. First methods to compute this and related notions for ID were computationally intractable for large-scale real-world datasets. In the present work, we derive a computationally feasible method for determining said axiomatic ID functions. Moreover, we demonstrate how the geometric properties of complex data are accounted for in our modeling. In particular, we propose a principle way to incorporate neighborhood information, as in graph data, into the ID. This allows for new insights into common graph learning procedures, which we illustrate by experiments on the Open Graph Benchmark.

  @article{stubbemann2022intrinsic,
  abstract = {The concept of dimension is essential to grasp the complexity of data. A naive approach to determine the dimension of a dataset is based on the number of attributes. More sophisticated methods derive a notion of intrinsic dimension (ID) that employs more complex feature functions, e.g., distances between data points. Yet, many of these approaches are based on empirical observations, cannot cope with the geometric character of contemporary datasets, and do lack an axiomatic foundation. A different approach was proposed by V. Pestov, who links the intrinsic dimension axiomatically to the mathematical concentration of measure phenomenon. First methods to compute this and related notions for ID were computationally intractable for large-scale real-world datasets. In the present work, we derive a computationally feasible method for determining said axiomatic ID functions. Moreover, we demonstrate how the geometric properties of complex data are accounted for in our modeling. In particular, we propose a principle way to incorporate neighborhood information, as in graph data, into the ID. This allows for new insights into common graph learning procedures, which we illustrate by experiments on the Open Graph Benchmark.},
  author = {Stubbemann, Maximilian and Hanika, Tom and Schneider, Friedrich Martin},
  journal = {Transactions on Machine Learning Research},
  keywords = {publist},
  title = {Intrinsic Dimension for Large-Scale Geometric Learning},
  year = 2023
  }
  

  %0 Journal Article
  %1 stubbemann2022intrinsic
  %A Stubbemann, Maximilian
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %D 2023
  %J Transactions on Machine Learning Research
  %T Intrinsic Dimension for Large-Scale Geometric Learning
  %U https://openreview.net/forum?id=85BfDdYMBY
  %X The concept of dimension is essential to grasp the complexity of data. A naive approach to determine the dimension of a dataset is based on the number of attributes. More sophisticated methods derive a notion of intrinsic dimension (ID) that employs more complex feature functions, e.g., distances between data points. Yet, many of these approaches are based on empirical observations, cannot cope with the geometric character of contemporary datasets, and do lack an axiomatic foundation. A different approach was proposed by V. Pestov, who links the intrinsic dimension axiomatically to the mathematical concentration of measure phenomenon. First methods to compute this and related notions for ID were computationally intractable for large-scale real-world datasets. In the present work, we derive a computationally feasible method for determining said axiomatic ID functions. Moreover, we demonstrate how the geometric properties of complex data are accounted for in our modeling. In particular, we propose a principle way to incorporate neighborhood information, as in graph data, into the ID. This allows for new insights into common graph learning procedures, which we illustrate by experiments on the Open Graph Benchmark.
  

• 
  Schäfermeier, B., Hirth, J., Hanika, T.: Research Topic Flows in Co-Authorship Networks Scientometrics. 128, 5051–5078 (2023).

  AbstractBibTeXEndNoteDOIBibSonomyDownload

  In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

  @article{schafermeier2022research,
  abstract = {In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.},
  author = {Schäfermeier, Bastian and Hirth, Johannes and Hanika, Tom},
  journal = {Scientometrics},
  keywords = {topic-models},
  month = {09},
  number = 9,
  pages = {5051--5078},
  title = {Research Topic Flows in Co-Authorship Networks},
  volume = 128,
  year = 2023
  }
  

  %0 Journal Article
  %1 schafermeier2022research
  %A Schäfermeier, Bastian
  %A Hirth, Johannes
  %A Hanika, Tom
  %D 2023
  %J Scientometrics
  %N 9
  %P 5051--5078
  %R 10.1007/s11192-022-04529-w
  %T Research Topic Flows in Co-Authorship Networks
  %V 128
  %X In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.
  

• 
  Ganter, B., Hanika, T., Hirth, J.: Scaling Dimension In: Dürrschnabel, D. and López-Rodríguez, D. (eds.) Formal Concept Analysis - 17th International Conference, {ICFCA} 2023, Kassel, Germany, July 17-21, 2023, Proceedings. pp. 64–77. Springer (2023).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/icfca/GanterHH23,
  author = {Ganter, Bernhard and Hanika, Tom and Hirth, Johannes},
  booktitle = {Formal Concept Analysis - 17th International Conference, {ICFCA} 2023, Kassel, Germany, July 17-21, 2023, Proceedings},
  editor = {Dürrschnabel, Dominik and López-Rodríguez, Domingo},
  keywords = {publist},
  pages = {64--77},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Scaling Dimension},
  volume = 13934,
  year = 2023
  }
  

  %0 Conference Paper
  %1 DBLP:conf/icfca/GanterHH23
  %A Ganter, Bernhard
  %A Hanika, Tom
  %A Hirth, Johannes
  %B Formal Concept Analysis - 17th International Conference, {ICFCA} 2023, Kassel, Germany, July 17-21, 2023, Proceedings
  %D 2023
  %E Dürrschnabel, Dominik
  %E López-Rodríguez, Domingo
  %I Springer
  %P 64--77
  %R 10.1007/978-3-031-35949-1\_5
  %T Scaling Dimension
  %U https://doi.org/10.1007/978-3-031-35949-1\_5
  %V 13934
  

2022

• 
  Hirth, J., Hanika, T.: Formal Conceptual Views in Neural Networks, http://arxiv.org/abs/2209.13517, (2022).

  AbstractURLBibTeXEndNoteBibSonomyDownload

  Explaining neural network models is a challenging task that remains unsolved in its entirety to this day. This is especially true for high dimensional and complex data. With the present work, we introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. Both provide novel analysis methods to enable a human AI analyst to grasp deeper insights into the knowledge that is captured by the neurons of a network. We test the conceptual expressivity of our novel views through different experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to which extent the views allow to quantify the conceptual similarity of different learning architectures. Finally, we demonstrate how conceptual views can be applied for abductive learning of human comprehensible rules from neurons. In summary, with our work, we contribute to the most relevant task of globally explaining neural networks models.

  @misc{hirth2022formal,
  abstract = {Explaining neural network models is a challenging task that remains unsolved in its entirety to this day. This is especially true for high dimensional and complex data. With the present work, we introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. Both provide novel analysis methods to enable a human AI analyst to grasp deeper insights into the knowledge that is captured by the neurons of a network. We test the conceptual expressivity of our novel views through different experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to which extent the views allow to quantify the conceptual similarity of different learning architectures. Finally, we demonstrate how conceptual views can be applied for abductive learning of human comprehensible rules from neurons. In summary, with our work, we contribute to the most relevant task of globally explaining neural networks models.},
  author = {Hirth, Johannes and Hanika, Tom},
  keywords = {views},
  note = {cite arxiv:2209.13517Comment: 17 pages, 8 figures, 9 tables},
  title = {Formal Conceptual Views in Neural Networks},
  year = 2022
  }
  

  %0 Generic
  %1 hirth2022formal
  %A Hirth, Johannes
  %A Hanika, Tom
  %D 2022
  %T Formal Conceptual Views in Neural Networks
  %U http://arxiv.org/abs/2209.13517
  %X Explaining neural network models is a challenging task that remains unsolved in its entirety to this day. This is especially true for high dimensional and complex data. With the present work, we introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. Both provide novel analysis methods to enable a human AI analyst to grasp deeper insights into the knowledge that is captured by the neurons of a network. We test the conceptual expressivity of our novel views through different experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to which extent the views allow to quantify the conceptual similarity of different learning architectures. Finally, we demonstrate how conceptual views can be applied for abductive learning of human comprehensible rules from neurons. In summary, with our work, we contribute to the most relevant task of globally explaining neural networks models.
  

• 
  Hanika, T., Hirth, J.: On the lattice of conceptual measurements Information Sciences. 613, 453–468 (2022).

  AbstractURLBibTeXEndNoteDOIBibSonomy

  We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.

  @article{HANIKA2022453,
  abstract = {We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.},
  author = {Hanika, Tom and Hirth, Johannes},
  journal = {Information Sciences},
  keywords = {scaling},
  pages = {453-468},
  title = {On the lattice of conceptual measurements},
  volume = 613,
  year = 2022
  }
  

  %0 Journal Article
  %1 HANIKA2022453
  %A Hanika, Tom
  %A Hirth, Johannes
  %D 2022
  %J Information Sciences
  %P 453-468
  %R https://doi.org/10.1016/j.ins.2022.09.005
  %T On the lattice of conceptual measurements
  %U https://www.sciencedirect.com/science/article/pii/S0020025522010489
  %V 613
  %X We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.
  

• 
  D{{ü}}rrschnabel, D., Hanika, T., Stubbemann, M.: {FCA2VEC:} Embedding Techniques for Formal Concept Analysis In: Missaoui, R., Kwuida, L., and Abdessalem, T. (eds.) Complex Data Analytics with Formal Concept Analysis. pp. 47–74. Springer International Publishing (2022).

  URLBibTeXEndNoteDOIBibSonomy

  @incollection{DBLP:books/sp/missaoui2022/DurrschnabelHS22,
  author = {D{{ü}}rrschnabel, Dominik and Hanika, Tom and Stubbemann, Maximilian},
  booktitle = {Complex Data Analytics with Formal Concept Analysis},
  editor = {Missaoui, Rokia and Kwuida, L{{é}}onard and Abdessalem, Talel},
  keywords = {sys:relevantfor:L3S},
  pages = {47--74},
  publisher = {Springer International Publishing},
  title = {{FCA2VEC:} Embedding Techniques for Formal Concept Analysis},
  year = 2022
  }
  

  %0 Book Section
  %1 DBLP:books/sp/missaoui2022/DurrschnabelHS22
  %A D{{ü}}rrschnabel, Dominik
  %A Hanika, Tom
  %A Stubbemann, Maximilian
  %B Complex Data Analytics with Formal Concept Analysis
  %D 2022
  %E Missaoui, Rokia
  %E Kwuida, L{{é}}onard
  %E Abdessalem, Talel
  %I Springer International Publishing
  %P 47--74
  %R 10.1007/978-3-030-93278-7_3
  %T {FCA2VEC:} Embedding Techniques for Formal Concept Analysis
  %U https://doi.org/10.1007/978-3-030-93278-7_3
  

• 
  Schäfermeier, B., Stumme, G., Hanika, T.: Mapping Research Trajectories, https://arxiv.org/abs/2204.11859, (2022).

  URLBibTeXEndNoteDOIBibSonomyDownload

  @misc{https://doi.org/10.48550/arxiv.2204.11859,
  author = {Schäfermeier, Bastian and Stumme, Gerd and Hanika, Tom},
  keywords = {trajectories},
  publisher = {arXiv},
  title = {Mapping Research Trajectories},
  year = 2022
  }
  

  %0 Generic
  %1 https://doi.org/10.48550/arxiv.2204.11859
  %A Schäfermeier, Bastian
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2022
  %I arXiv
  %R 10.48550/ARXIV.2204.11859
  %T Mapping Research Trajectories
  %U https://arxiv.org/abs/2204.11859
  

• 
  Hanika, T., Hirth, J.: Knowledge cores in large formal contexts Annals of Mathematics and Artificial Intelligence. 90, 537–567 (2022).

  AbstractURLBibTeXEndNoteDOIBibSonomy

  Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.

  @article{Hanika2022,
  abstract = {Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.},
  author = {Hanika, Tom and Hirth, Johannes},
  journal = {Annals of Mathematics and Artificial Intelligence},
  keywords = {publist},
  month = {apr},
  number = 6,
  pages = {537--567},
  title = {Knowledge cores in large formal contexts},
  volume = 90,
  year = 2022
  }
  

  %0 Journal Article
  %1 Hanika2022
  %A Hanika, Tom
  %A Hirth, Johannes
  %D 2022
  %J Annals of Mathematics and Artificial Intelligence
  %N 6
  %P 537--567
  %R 10.1007/s10472-022-09790-6
  %T Knowledge cores in large formal contexts
  %U https://doi.org/10.1007/s10472-022-09790-6
  %V 90
  %X Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.
  

• 
  Hanika, T., Schneider, F.M., Stumme, G.: {Intrinsic dimension of geometric data sets} Tohoku Mathematical Journal. 74, 23–52 (2022).

  AbstractURLBibTeXEndNoteDOIBibSonomy

  The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.

  @article{10.2748/tmj.20201015a,
  abstract = {The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.},
  author = {Hanika, Tom and Schneider, Friedrich Martin and Stumme, Gerd},
  journal = {Tohoku Mathematical Journal},
  keywords = {publist},
  number = 1,
  pages = {23 -- 52},
  publisher = {Tohoku University, Mathematical Institute},
  title = {{Intrinsic dimension of geometric data sets}},
  volume = 74,
  year = 2022
  }
  

  %0 Journal Article
  %1 10.2748/tmj.20201015a
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %A Stumme, Gerd
  %D 2022
  %I Tohoku University, Mathematical Institute
  %J Tohoku Mathematical Journal
  %N 1
  %P 23 -- 52
  %R 10.2748/tmj.20201015a
  %T {Intrinsic dimension of geometric data sets}
  %U https://doi.org/10.2748/tmj.20201015a
  %V 74
  %X The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
  

2021

• 
  Braud, A., Buzmakov, A., Hanika, T., Ber, F.L. eds.: Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings Springer (2021).

  URLBibTeXEndNoteDOIBibSonomy

  @proceedings{DBLP:conf/icfca/2021,
  editor = {Braud, Agn{{è}}s and Buzmakov, Aleksey and Hanika, Tom and Ber, Florence Le},
  keywords = {publist},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings},
  volume = 12733,
  year = 2021
  }
  

  %0 Conference Proceedings
  %1 DBLP:conf/icfca/2021
  %B Lecture Notes in Computer Science
  %D 2021
  %E Braud, Agn{{è}}s
  %E Buzmakov, Aleksey
  %E Hanika, Tom
  %E Ber, Florence Le
  %I Springer
  %R 10.1007/978-3-030-77867-5
  %T Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings
  %U https://doi.org/10.1007/978-3-030-77867-5
  %V 12733
  %@ 978-3-030-77866-8
  

• 
  Schaefermeier, B., Stumme, G., Hanika, T.: Topic space trajectories Scientometrics. 126, 5759–5795 (2021).

  AbstractURLBibTeXEndNoteDOIBibSonomyDownload

  The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.

  @article{Schaefermeier2021,
  abstract = {The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.},
  author = {Schaefermeier, Bastian and Stumme, Gerd and Hanika, Tom},
  journal = {Scientometrics},
  keywords = {topic},
  month = {may},
  number = 7,
  pages = {5759–5795},
  title = {Topic space trajectories},
  volume = 126,
  year = 2021
  }
  

  %0 Journal Article
  %1 Schaefermeier2021
  %A Schaefermeier, Bastian
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2021
  %J Scientometrics
  %N 7
  %P 5759–5795
  %R 10.1007/s11192-021-03931-0
  %T Topic space trajectories
  %U https://doi.org/10.1007/s11192-021-03931-0
  %V 126
  %X The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
  

• 
  Schaefermeier, B., Stumme, G., Hanika, T.: Topological Indoor Mapping through WiFi Signals (2021).

  AbstractURLBibTeXEndNoteBibSonomyDownload

  The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.

  @article{schaefermeier2021topological,
  abstract = {The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.},
  author = {Schaefermeier, Bastian and Stumme, Gerd and Hanika, Tom},
  keywords = {wifi},
  note = {cite arxiv:2106.09789Comment: 18 pages},
  title = {Topological Indoor Mapping through WiFi Signals},
  year = 2021
  }
  

  %0 Journal Article
  %1 schaefermeier2021topological
  %A Schaefermeier, Bastian
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2021
  %T Topological Indoor Mapping through WiFi Signals
  %U http://arxiv.org/abs/2106.09789
  %X The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.
  

• 
  Hanika, T., Hirth, J.: Quantifying the Conceptual Error in Dimensionality Reduction In: Braun, T., Gehrke, M., Hanika, T., and Hernandez, N. (eds.) Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings. pp. 105–118. Springer (2021).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/iccs/HanikaH21,
  author = {Hanika, Tom and Hirth, Johannes},
  booktitle = {Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings},
  editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},
  keywords = {scaling},
  pages = {105--118},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Quantifying the Conceptual Error in Dimensionality Reduction},
  volume = 12879,
  year = 2021
  }
  

  %0 Conference Paper
  %1 DBLP:conf/iccs/HanikaH21
  %A Hanika, Tom
  %A Hirth, Johannes
  %B Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings
  %D 2021
  %E Braun, Tanya
  %E Gehrke, Marcel
  %E Hanika, Tom
  %E Hernandez, Nathalie
  %I Springer
  %P 105--118
  %R 10.1007/978-3-030-86982-3_8
  %T Quantifying the Conceptual Error in Dimensionality Reduction
  %U https://doi.org/10.1007/978-3-030-86982-3_8
  %V 12879
  

• 
  Hanika, T., Hirth, J.: Exploring Scale-Measures of Data Sets In: Braud, A., Buzmakov, A., Hanika, T., and Ber, F.L. (eds.) Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings. pp. 261–269. Springer (2021).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/icfca/HanikaH21,
  author = {Hanika, Tom and Hirth, Johannes},
  booktitle = {Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings},
  editor = {Braud, Agn{{è}}s and Buzmakov, Aleksey and Hanika, Tom and Ber, Florence Le},
  keywords = {scaling},
  pages = {261--269},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Exploring Scale-Measures of Data Sets},
  volume = 12733,
  year = 2021
  }
  

  %0 Conference Paper
  %1 DBLP:conf/icfca/HanikaH21
  %A Hanika, Tom
  %A Hirth, Johannes
  %B Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings
  %D 2021
  %E Braud, Agn{{è}}s
  %E Buzmakov, Aleksey
  %E Hanika, Tom
  %E Ber, Florence Le
  %I Springer
  %P 261--269
  %R 10.1007/978-3-030-77867-5_17
  %T Exploring Scale-Measures of Data Sets
  %U https://doi.org/10.1007/978-3-030-77867-5_17
  %V 12733
  

• 
  Braun, T., Gehrke, M., Hanika, T., Hernandez, N. eds.: Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings Springer (2021).

  URLBibTeXEndNoteDOIBibSonomy

  @proceedings{DBLP:conf/iccs/2021,
  editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},
  keywords = {publist},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings},
  volume = 12879,
  year = 2021
  }
  

  %0 Conference Proceedings
  %1 DBLP:conf/iccs/2021
  %B Lecture Notes in Computer Science
  %D 2021
  %E Braun, Tanya
  %E Gehrke, Marcel
  %E Hanika, Tom
  %E Hernandez, Nathalie
  %I Springer
  %R 10.1007/978-3-030-86982-3
  %T Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings
  %U https://doi.org/10.1007/978-3-030-86982-3
  %V 12879
  %@ 978-3-030-86981-6
  

• 
  Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jäschke, R., Stumme, G.: Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research Scientometrics. (2021).

  AbstractURLBibTeXEndNoteDOIBibSonomy

  Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.

  @article{koopmann2021proximity,
  abstract = {Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.},
  author = {Koopmann, Tobias and Stubbemann, Maximilian and Kapa, Matthias and Paris, Michael and Buenstorf, Guido and Hanika, Tom and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  journal = {Scientometrics},
  keywords = {regio},
  month = {mar},
  title = {Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research},
  year = 2021
  }
  

  %0 Journal Article
  %1 koopmann2021proximity
  %A Koopmann, Tobias
  %A Stubbemann, Maximilian
  %A Kapa, Matthias
  %A Paris, Michael
  %A Buenstorf, Guido
  %A Hanika, Tom
  %A Hotho, Andreas
  %A Jäschke, Robert
  %A Stumme, Gerd
  %D 2021
  %J Scientometrics
  %R 10.1007/s11192-021-03922-1
  %T Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research
  %U https://link.springer.com/article/10.1007/s11192-021-03922-1
  %X Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.
  

2020

• 
  Stubbemann, M., Hanika, T., Stumme, G.: Orometric Methods in Bounded Metric Data In: Berthold, M.R., Feelders, A., and Krempl, G. (eds.) Advances in Intelligent Data Analysis {XVIII} - 18th International Symposium on Intelligent Data Analysis, {IDA} 2020, Konstanz, Germany, April 27-29, 2020, Proceedings. pp. 496–508. Springer (2020).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/ida/StubbemannHS20,
  author = {Stubbemann, Maximilian and Hanika, Tom and Stumme, Gerd},
  booktitle = {Advances in Intelligent Data Analysis {XVIII} - 18th International Symposium on Intelligent Data Analysis, {IDA} 2020, Konstanz, Germany, April 27-29, 2020, Proceedings},
  editor = {Berthold, Michael R. and Feelders, Ad and Krempl, Georg},
  keywords = {publist},
  pages = {496--508},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Orometric Methods in Bounded Metric Data},
  volume = 12080,
  year = 2020
  }
  

  %0 Conference Paper
  %1 DBLP:conf/ida/StubbemannHS20
  %A Stubbemann, Maximilian
  %A Hanika, Tom
  %A Stumme, Gerd
  %B Advances in Intelligent Data Analysis {XVIII} - 18th International Symposium on Intelligent Data Analysis, {IDA} 2020, Konstanz, Germany, April 27-29, 2020, Proceedings
  %D 2020
  %E Berthold, Michael R.
  %E Feelders, Ad
  %E Krempl, Georg
  %I Springer
  %P 496--508
  %R 10.1007/978-3-030-44584-3_39
  %T Orometric Methods in Bounded Metric Data
  %U https://doi.org/10.1007/978-3-030-44584-3\_39
  %V 12080
  

• 
  Felde, M., Hanika, T., Stumme, G.: Null Models for Formal Contexts Information. 11, 135 (2020).

  URLBibTeXEndNoteBibSonomyDownload

  @article{felde2020null,
  author = {Felde, Maximilian and Hanika, Tom and Stumme, Gerd},
  journal = {Information},
  keywords = {publist},
  number = 3,
  pages = 135,
  publisher = {Multidisciplinary Digital Publishing Institute},
  title = {Null Models for Formal Contexts},
  volume = 11,
  year = 2020
  }
  

  %0 Journal Article
  %1 felde2020null
  %A Felde, Maximilian
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2020
  %I Multidisciplinary Digital Publishing Institute
  %J Information
  %N 3
  %P 135
  %T Null Models for Formal Contexts
  %U https://www.mdpi.com/2078-2489/11/3/135
  %V 11
  

• 
  Borchmann, D., Hanika, T., Obiedkov, S.: Probably approximately correct learning of Horn envelopes from queries Discrete Applied Mathematics. 273, 30–42 (2020).

  AbstractURLBibTeXEndNoteDOIBibSonomy

  We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.

  @article{BORCHMANN202030,
  abstract = {We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.},
  author = {Borchmann, Daniel and Hanika, Tom and Obiedkov, Sergei},
  journal = {Discrete Applied Mathematics},
  keywords = {publist},
  note = {Advances in Formal Concept Analysis: Traces of CLA 2016},
  pages = {30 - 42},
  title = {Probably approximately correct learning of Horn envelopes from queries},
  volume = 273,
  year = 2020
  }
  

  %0 Journal Article
  %1 BORCHMANN202030
  %A Borchmann, Daniel
  %A Hanika, Tom
  %A Obiedkov, Sergei
  %D 2020
  %J Discrete Applied Mathematics
  %P 30 - 42
  %R https://doi.org/10.1016/j.dam.2019.02.036
  %T Probably approximately correct learning of Horn envelopes from queries
  %U http://www.sciencedirect.com/science/article/pii/S0166218X19301295
  %V 273
  %X We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.
  

2019

• 
  Hanika, T., Koyda, M., Stumme, G.: Relevant Attributes in Formal Contexts. In: Endres, D., Alam, M., and Sotropa, D. (eds.) ICCS. pp. 102–116. Springer (2019).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/iccs/HanikaKS19,
  author = {Hanika, Tom and Koyda, Maren and Stumme, Gerd},
  booktitle = {ICCS},
  crossref = {conf/iccs/2019},
  editor = {Endres, Dominik and Alam, Mehwish and Sotropa, Diana},
  keywords = {publist},
  pages = {102-116},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Relevant Attributes in Formal Contexts.},
  volume = 11530,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/iccs/HanikaKS19
  %A Hanika, Tom
  %A Koyda, Maren
  %A Stumme, Gerd
  %B ICCS
  %D 2019
  %E Endres, Dominik
  %E Alam, Mehwish
  %E Sotropa, Diana
  %I Springer
  %P 102-116
  %R 10.1007/978-3-030-23182-8_8
  %T Relevant Attributes in Formal Contexts.
  %U http://dblp.uni-trier.de/db/conf/iccs/iccs2019.html#HanikaKS19
  %V 11530
  %@ 978-3-030-23182-8
  

• 
  Hanika, T., Kibanov, M., Kropf, J., Laser, S.: Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt popul{ä}r und besonders interessant f{ü}r die Forschung geworden ist. In: Kropf, J. and Laser, S. (eds.) Digitale Bewertungspraktiken. pp. 165–188. Springer (2019).

  BibTeXEndNoteBibSonomy

  @incollection{hanika2019denke,
  author = {Hanika, Tom and Kibanov, Mark and Kropf, Jonathan and Laser, Stefan},
  booktitle = {Digitale Bewertungspraktiken},
  editor = {Kropf, Jonathan and Laser, Stefan},
  keywords = {sociology},
  pages = {165--188},
  publisher = {Springer},
  title = {Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt popul{ä}r und besonders interessant f{ü}r die Forschung geworden ist.},
  year = 2019
  }
  

  %0 Book Section
  %1 hanika2019denke
  %A Hanika, Tom
  %A Kibanov, Mark
  %A Kropf, Jonathan
  %A Laser, Stefan
  %B Digitale Bewertungspraktiken
  %D 2019
  %E Kropf, Jonathan
  %E Laser, Stefan
  %I Springer
  %P 165--188
  %T Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt popul{ä}r und besonders interessant f{ü}r die Forschung geworden ist.
  

• 
  Hanika, T., Herde, M., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning - A clarification of terms and a differentiation from other research fields. CoRR. abs/1905.07264, (2019).

  URLBibTeXEndNoteBibSonomy

  @article{journals/corr/abs-1905-07264,
  author = {Hanika, Tom and Herde, Marek and Kuhn, Jochen and Leimeister, Jan Marco and Lukowicz, Paul and Oeste-Reiß, Sarah and Schmidt, Albrecht and Sick, Bernhard and Stumme, Gerd and Tomforde, Sven and Zweig, Katharina Anna},
  journal = {CoRR},
  keywords = {publist},
  title = {Collaborative Interactive Learning - A clarification of terms and a differentiation from other research fields.},
  volume = {abs/1905.07264},
  year = 2019
  }
  

  %0 Journal Article
  %1 journals/corr/abs-1905-07264
  %A Hanika, Tom
  %A Herde, Marek
  %A Kuhn, Jochen
  %A Leimeister, Jan Marco
  %A Lukowicz, Paul
  %A Oeste-Reiß, Sarah
  %A Schmidt, Albrecht
  %A Sick, Bernhard
  %A Stumme, Gerd
  %A Tomforde, Sven
  %A Zweig, Katharina Anna
  %D 2019
  %J CoRR
  %T Collaborative Interactive Learning - A clarification of terms and a differentiation from other research fields.
  %U http://dblp.uni-trier.de/db/journals/corr/corr1905.html#abs-1905-07264
  %V abs/1905.07264
  

• 
  Hanika, T., Marx, M., Stumme, G.: Discovering Implicational Knowledge in Wikidata. In: Cristea, D., Ber, F.L., and Sertkaya, B. (eds.) ICFCA. pp. 315–323. Springer (2019).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/icfca/Hanika0S19,
  author = {Hanika, Tom and Marx, Maximilian and Stumme, Gerd},
  booktitle = {ICFCA},
  crossref = {conf/icfca/2019},
  editor = {Cristea, Diana and Ber, Florence Le and Sertkaya, Baris},
  keywords = {publist},
  pages = {315-323},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Discovering Implicational Knowledge in Wikidata.},
  volume = 11511,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/icfca/Hanika0S19
  %A Hanika, Tom
  %A Marx, Maximilian
  %A Stumme, Gerd
  %B ICFCA
  %D 2019
  %E Cristea, Diana
  %E Ber, Florence Le
  %E Sertkaya, Baris
  %I Springer
  %P 315-323
  %R 10.1007/978-3-030-21462-3_21
  %T Discovering Implicational Knowledge in Wikidata.
  %U http://dblp.uni-trier.de/db/conf/icfca/icfca2019.html#Hanika0S19
  %V 11511
  %@ 978-3-030-21462-3
  

• 
  Dürrschnabel, D., Hanika, T., Stumme, G.: Drawing Order Diagrams Through Two-Dimension Extension, http://arxiv.org/abs/1906.06208, (2019).

  AbstractURLBibTeXEndNoteBibSonomy

  Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work we present the novel algorithm DimDraw to draw order diagrams. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of a corresponding graph.

  @misc{durrschnabel2019drawing,
  abstract = {Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work we present the novel algorithm DimDraw to draw order diagrams. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of a corresponding graph.},
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stumme, Gerd},
  keywords = {publist},
  note = {cite arxiv:1906.06208Comment: 16 pages, 12 Figures},
  title = {Drawing Order Diagrams Through Two-Dimension Extension},
  year = 2019
  }
  

  %0 Generic
  %1 durrschnabel2019drawing
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2019
  %T Drawing Order Diagrams Through Two-Dimension Extension
  %U http://arxiv.org/abs/1906.06208
  %X Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work we present the novel algorithm DimDraw to draw order diagrams. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of a corresponding graph.
  

• 
  Hanika, T., Marx, M., Stumme, G.: Discovering Implicational Knowledge in Wikidata In: Cristea, D., Ber, F.L., and Sertkaya, B. (eds.) Formal Concept Analysis - 15th International Conference, {ICFCA} 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings. pp. 315–323. Springer (2019).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/icfca/Hanika0S19,
  author = {Hanika, Tom and Marx, Maximilian and Stumme, Gerd},
  booktitle = {Formal Concept Analysis - 15th International Conference, {ICFCA} 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings},
  editor = {Cristea, Diana and Ber, Florence Le and Sertkaya, Baris},
  keywords = {publist},
  pages = {315--323},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Discovering Implicational Knowledge in Wikidata},
  volume = 11511,
  year = 2019
  }
  

  %0 Conference Paper
  %1 DBLP:conf/icfca/Hanika0S19
  %A Hanika, Tom
  %A Marx, Maximilian
  %A Stumme, Gerd
  %B Formal Concept Analysis - 15th International Conference, {ICFCA} 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings
  %D 2019
  %E Cristea, Diana
  %E Ber, Florence Le
  %E Sertkaya, Baris
  %I Springer
  %P 315--323
  %R 10.1007/978-3-030-21462-3_21
  %T Discovering Implicational Knowledge in Wikidata
  %U https://doi.org/10.1007/978-3-030-21462-3_21
  %V 11511
  

• 
  Felde, M., Hanika, T.: Formal Context Generation Using Dirichlet Distributions. In: Endres, D., Alam, M., and Sotropa, D. (eds.) ICCS. pp. 57–71. Springer (2019).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/iccs/FeldeH19,
  author = {Felde, Maximilian and Hanika, Tom},
  booktitle = {ICCS},
  crossref = {conf/iccs/2019},
  editor = {Endres, Dominik and Alam, Mehwish and Sotropa, Diana},
  keywords = {random},
  pages = {57-71},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Formal Context Generation Using Dirichlet Distributions.},
  volume = 11530,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/iccs/FeldeH19
  %A Felde, Maximilian
  %A Hanika, Tom
  %B ICCS
  %D 2019
  %E Endres, Dominik
  %E Alam, Mehwish
  %E Sotropa, Diana
  %I Springer
  %P 57-71
  %R 10.1007/978-3-030-23182-8_5
  %T Formal Context Generation Using Dirichlet Distributions.
  %U http://dblp.uni-trier.de/db/conf/iccs/iccs2019.html#FeldeH19
  %V 11530
  %@ 978-3-030-23182-8
  

• 
  Hanika, T., Hirth, J.: Conexp-Clj - A Research Tool for FCA. In: Cristea, D., Ber, F.L., Missaoui, R., Kwuida, L., and Sertkaya, B. (eds.) ICFCA (Supplements). pp. 70–75. CEUR-WS.org (2019).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{conf/icfca/HanikaH19,
  author = {Hanika, Tom and Hirth, Johannes},
  booktitle = {ICFCA (Supplements)},
  crossref = {conf/icfca/2019suppl},
  editor = {Cristea, Diana and Ber, Florence Le and Missaoui, Rokia and Kwuida, Léonard and Sertkaya, Baris},
  keywords = {publist},
  pages = {70-75},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {Conexp-Clj - A Research Tool for FCA.},
  volume = 2378,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/icfca/HanikaH19
  %A Hanika, Tom
  %A Hirth, Johannes
  %B ICFCA (Supplements)
  %D 2019
  %E Cristea, Diana
  %E Ber, Florence Le
  %E Missaoui, Rokia
  %E Kwuida, Léonard
  %E Sertkaya, Baris
  %I CEUR-WS.org
  %P 70-75
  %T Conexp-Clj - A Research Tool for FCA.
  %U http://dblp.uni-trier.de/db/conf/icfca/icfca2019suppl.html#HanikaH19
  %V 2378
  

• 
  Dürrschnabel, D., Hanika, T., Stumme, G.: DimDraw - A Novel Tool for Drawing Concept Lattices. In: Cristea, D., Ber, F.L., Missaoui, R., Kwuida, L., and Sertkaya, B. (eds.) ICFCA (Supplements). pp. 60–64. CEUR-WS.org (2019).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{conf/icfca/DurrschnabelHS19,
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stumme, Gerd},
  booktitle = {ICFCA (Supplements)},
  crossref = {conf/icfca/2019suppl},
  editor = {Cristea, Diana and Ber, Florence Le and Missaoui, Rokia and Kwuida, Léonard and Sertkaya, Baris},
  keywords = {publist},
  pages = {60-64},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {DimDraw - A Novel Tool for Drawing Concept Lattices.},
  volume = 2378,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/icfca/DurrschnabelHS19
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stumme, Gerd
  %B ICFCA (Supplements)
  %D 2019
  %E Cristea, Diana
  %E Ber, Florence Le
  %E Missaoui, Rokia
  %E Kwuida, Léonard
  %E Sertkaya, Baris
  %I CEUR-WS.org
  %P 60-64
  %T DimDraw - A Novel Tool for Drawing Concept Lattices.
  %U http://dblp.uni-trier.de/db/conf/icfca/icfca2019suppl.html#DurrschnabelHS19
  %V 2378
  

• 
  Hanika, T.: Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis., (2019).

  BibTeXEndNoteDOIBibSonomy

  @phdthesis{phd/dnb/Hanika19,
  author = {Hanika, Tom},
  keywords = {tag},
  school = {University of Kassel, Germany},
  title = {Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis.},
  year = 2019
  }
  

  %0 Thesis
  %1 phd/dnb/Hanika19
  %A Hanika, Tom
  %D 2019
  %R 10.17170/kobra-20190213189
  %T Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis.
  

• 
  Schaefermeier, B., Hanika, T., Stumme, G.: Distances for wifi based topological indoor mapping Proceedings of the 16th {EAI} International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. {ACM} (2019).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{Schaefermeier_2019,
  author = {Schaefermeier, Bastian and Hanika, Tom and Stumme, Gerd},
  booktitle = {Proceedings of the 16th {EAI} International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
  keywords = {wifi},
  month = {nov},
  publisher = {{ACM}},
  title = {Distances for wifi based topological indoor mapping},
  year = 2019
  }
  

  %0 Conference Paper
  %1 Schaefermeier_2019
  %A Schaefermeier, Bastian
  %A Hanika, Tom
  %A Stumme, Gerd
  %B Proceedings of the 16th {EAI} International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
  %D 2019
  %I {ACM}
  %R 10.1145/3360774.3360780
  %T Distances for wifi based topological indoor mapping
  %U https://doi.org/10.1145%2F3360774.3360780
  

2018

• 
  Doerfel, S., Hanika, T., Stumme, G.: Clones in Graphs. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G.A., and Ras, Z.W. (eds.) ISMIS. pp. 56–66. Springer (2018).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/ismis/DoerfelHS18,
  author = {Doerfel, Stephan and Hanika, Tom and Stumme, Gerd},
  booktitle = {ISMIS},
  crossref = {conf/ismis/2018},
  editor = {Ceci, Michelangelo and Japkowicz, Nathalie and Liu, Jiming and Papadopoulos, George A. and Ras, Zbigniew W.},
  keywords = {publist},
  pages = {56-66},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Clones in Graphs.},
  volume = 11177,
  year = 2018
  }
  

  %0 Conference Paper
  %1 conf/ismis/DoerfelHS18
  %A Doerfel, Stephan
  %A Hanika, Tom
  %A Stumme, Gerd
  %B ISMIS
  %D 2018
  %E Ceci, Michelangelo
  %E Japkowicz, Nathalie
  %E Liu, Jiming
  %E Papadopoulos, George A.
  %E Ras, Zbigniew W.
  %I Springer
  %P 56-66
  %R 10.1007/978-3-030-01851-1_6
  %T Clones in Graphs.
  %U http://dblp.uni-trier.de/db/conf/ismis/ismis2018.html#DoerfelHS18
  %V 11177
  %@ 978-3-030-01851-1
  

• 
  Hanika, T., Zumbrägel, J.: Towards Collaborative Conceptual Exploration. In: Chapman, P., Endres, D., and Pernelle, N. (eds.) ICCS. pp. 120–134. Springer (2018).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/iccs/HanikaZ18,
  author = {Hanika, Tom and Zumbrägel, Jens},
  booktitle = {ICCS},
  crossref = {conf/iccs/2018},
  editor = {Chapman, Peter and Endres, Dominik and Pernelle, Nathalie},
  keywords = {publist},
  pages = {120-134},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Towards Collaborative Conceptual Exploration.},
  volume = 10872,
  year = 2018
  }
  

  %0 Conference Paper
  %1 conf/iccs/HanikaZ18
  %A Hanika, Tom
  %A Zumbrägel, Jens
  %B ICCS
  %D 2018
  %E Chapman, Peter
  %E Endres, Dominik
  %E Pernelle, Nathalie
  %I Springer
  %P 120-134
  %R 10.1007/978-3-319-91379-7_10
  %T Towards Collaborative Conceptual Exploration.
  %U http://dblp.uni-trier.de/db/conf/iccs/iccs2018.html#HanikaZ18
  %V 10872
  %@ 978-3-319-91379-7
  

2017

• 
  Borchmann, D., Hanika, T.: Individuality in Social Networks In: Missaoui, R., Kuznetsov, S.O., and Obiedkov, S. (eds.) Formal Concept Analysis of Social Networks. pp. 19–40. Springer International Publishing, Cham (2017).

  AbstractURLBibTeXEndNoteDOIBibSonomy

  We consider individuality in bi-modal social networks, a facet that has not been considered before in the mathematical analysis of social networks. We use methods from formal concept analysis to develop a natural definition for individuality, and provide experimental evidence that this yields a meaningful approach for additional insights into the nature of social networks.

  @inbook{Borchmann2017,
  abstract = {We consider individuality in bi-modal social networks, a facet that has not been considered before in the mathematical analysis of social networks. We use methods from formal concept analysis to develop a natural definition for individuality, and provide experimental evidence that this yields a meaningful approach for additional insights into the nature of social networks.},
  address = {Cham},
  author = {Borchmann, Daniel and Hanika, Tom},
  booktitle = {Formal Concept Analysis of Social Networks},
  editor = {Missaoui, Rokia and Kuznetsov, Sergei O. and Obiedkov, Sergei},
  keywords = {social},
  pages = {19--40},
  publisher = {Springer International Publishing},
  title = {Individuality in Social Networks},
  year = 2017
  }
  

  %0 Book Section
  %1 Borchmann2017
  %A Borchmann, Daniel
  %A Hanika, Tom
  %B Formal Concept Analysis of Social Networks
  %C Cham
  %D 2017
  %E Missaoui, Rokia
  %E Kuznetsov, Sergei O.
  %E Obiedkov, Sergei
  %I Springer International Publishing
  %P 19--40
  %R 10.1007/978-3-319-64167-6_2
  %T Individuality in Social Networks
  %U https://doi.org/10.1007/978-3-319-64167-6_2
  %X We consider individuality in bi-modal social networks, a facet that has not been considered before in the mathematical analysis of social networks. We use methods from formal concept analysis to develop a natural definition for individuality, and provide experimental evidence that this yields a meaningful approach for additional insights into the nature of social networks.
  %@ 978-3-319-64167-6
  

• 
  Borchmann, D., Hanika, T., Obiedkov, S.: On the Usability of Probably Approximately Correct Implication Bases. In: Bertet, K., Borchmann, D., Cellier, P., and Ferré, S. (eds.) Formal Concept Analysis - 14th International Conference, {ICFCA} 2017, Rennes, France, June 13-16, 2017, Proceedings. pp. 72–88. Springer (2017).

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/icfca/BorchmannHO17,
  author = {Borchmann, Daniel and Hanika, Tom and Obiedkov, Sergei},
  booktitle = {Formal Concept Analysis - 14th International Conference, {ICFCA} 2017, Rennes, France, June 13-16, 2017, Proceedings},
  crossref = {conf/icfca/2017},
  editor = {Bertet, Karell and Borchmann, Daniel and Cellier, Peggy and Ferré, Sébastien},
  keywords = {publist},
  pages = {72-88},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {On the Usability of Probably Approximately Correct Implication Bases.},
  volume = 10308,
  year = 2017
  }
  

  %0 Conference Paper
  %1 conf/icfca/BorchmannHO17
  %A Borchmann, Daniel
  %A Hanika, Tom
  %A Obiedkov, Sergei
  %B Formal Concept Analysis - 14th International Conference, {ICFCA} 2017, Rennes, France, June 13-16, 2017, Proceedings
  %D 2017
  %E Bertet, Karell
  %E Borchmann, Daniel
  %E Cellier, Peggy
  %E Ferré, Sébastien
  %I Springer
  %P 72-88
  %R 10.1007/978-3-319-59271-8_5
  %T On the Usability of Probably Approximately Correct Implication Bases.
  %U http://dblp.uni-trier.de/db/conf/icfca/icfca2017.html#BorchmannHO17
  %V 10308
  %@ 978-3-319-59271-8
  

2016

• 
  Atzmueller, M., Hanika, T., Stumme, G., Schaller, R., Ludwig, B.: {Social Event Network Analysis: Structure, Preferences, and Reality} Proc. IEEE/ACM ASONAM. IEEE Press, Boston, MA, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{langeNachtFCA,
  address = {Boston, MA, USA},
  author = {Atzmueller, Martin and Hanika, Tom and Stumme, Gerd and Schaller, Richard and Ludwig, Bernd},
  booktitle = {Proc. IEEE/ACM ASONAM},
  keywords = {publist},
  publisher = {IEEE Press},
  title = {{Social Event Network Analysis: Structure, Preferences, and Reality}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 langeNachtFCA
  %A Atzmueller, Martin
  %A Hanika, Tom
  %A Stumme, Gerd
  %A Schaller, Richard
  %A Ludwig, Bernd
  %B Proc. IEEE/ACM ASONAM
  %C Boston, MA, USA
  %D 2016
  %I IEEE Press
  %T {Social Event Network Analysis: Structure, Preferences, and Reality}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-social-event-analysis-asonam16-preprint.pdf
  

• 
  Borchmann, D., Hanika, T.: Some Experimental Results on Randomly Generating Formal Contexts. In: Huchard, M. and Kuznetsov, S. (eds.) CLA. pp. 57–69. CEUR-WS.org (2016).

  URLBibTeXEndNoteBibSonomyDownload

  @inproceedings{conf/cla/BorchmannH16,
  author = {Borchmann, Daniel and Hanika, Tom},
  booktitle = {CLA},
  crossref = {conf/cla/2016},
  editor = {Huchard, Marianne and Kuznetsov, Sergei},
  keywords = {stego},
  pages = {57-69},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {Some Experimental Results on Randomly Generating Formal Contexts.},
  volume = 1624,
  year = 2016
  }
  

  %0 Conference Paper
  %1 conf/cla/BorchmannH16
  %A Borchmann, Daniel
  %A Hanika, Tom
  %B CLA
  %D 2016
  %E Huchard, Marianne
  %E Kuznetsov, Sergei
  %I CEUR-WS.org
  %P 57-69
  %T Some Experimental Results on Randomly Generating Formal Contexts.
  %U http://dblp.uni-trier.de/db/conf/cla/cla2016.html#BorchmannH16
  %V 1624