Johannes Hirth
Raum 0445C
Universität Kassel
Fachbereich Elektrotechnik/Informatik
Fachgebiet Wissensverarbeitung
Wilhelmshöher Allee 73
34121 Kassel
Tel.: +49 561 804-6350
Fax.: +49 561 804-6259
Email: hirth@cs.uni-kassel.de
Publications
1.
Hanika, T., Hirth, J.: Exploring Scale-Measures of Data Sets, http://arxiv.org/abs/2102.02576, (2021).
URLBibTeXEndNoteBibSonomy
Measurement is a fundamental building block of numerous scientific models and their creation. This is in particular true for data driven science. Due to the high complexity and size of modern data sets, the necessity for the development of understandable and efficient scaling methods is at hand. A profound theory for scaling data is scale-measures, as developed in the field of formal concept analysis. Recent developments indicate that the set of all scale-measures for a given data set constitutes a lattice and does hence allow efficient exploring algorithms. In this work we study the properties of said lattice and propose a novel scale-measure exploration algorithm that is based on the well-known and proven attribute exploration approach. Our results motivate multiple applications in scale recommendation, most prominently (semi-)automatic scaling.
@misc{hanika2021exploring,
abstract = {Measurement is a fundamental building block of numerous scientific models and their creation. This is in particular true for data driven science. Due to the high complexity and size of modern data sets, the necessity for the development of understandable and efficient scaling methods is at hand. A profound theory for scaling data is scale-measures, as developed in the field of formal concept analysis. Recent developments indicate that the set of all scale-measures for a given data set constitutes a lattice and does hence allow efficient exploring algorithms. In this work we study the properties of said lattice and propose a novel scale-measure exploration algorithm that is based on the well-known and proven attribute exploration approach. Our results motivate multiple applications in scale recommendation, most prominently (semi-)automatic scaling.},
author = {Hanika, Tom and Hirth, Johannes},
keywords = {data fca myown preprocessing scale-measure scaling},
note = {cite arxiv:2102.02576Comment: 16 pages, 5 figures},
title = {Exploring Scale-Measures of Data Sets},
year = 2021
}
%0 Generic
%1 hanika2021exploring
%A Hanika, Tom
%A Hirth, Johannes
%D 2021
%T Exploring Scale-Measures of Data Sets
%U http://arxiv.org/abs/2102.02576
%X Measurement is a fundamental building block of numerous scientific models and their creation. This is in particular true for data driven science. Due to the high complexity and size of modern data sets, the necessity for the development of understandable and efficient scaling methods is at hand. A profound theory for scaling data is scale-measures, as developed in the field of formal concept analysis. Recent developments indicate that the set of all scale-measures for a given data set constitutes a lattice and does hence allow efficient exploring algorithms. In this work we study the properties of said lattice and propose a novel scale-measure exploration algorithm that is based on the well-known and proven attribute exploration approach. Our results motivate multiple applications in scale recommendation, most prominently (semi-)automatic scaling.
1.
Hanika, T., Hirth, J.: Quantifying the Conceptual Error in Dimensionality Reduction. Accepted for publication at ICCS 2021. (2021).
URLBibTeXEndNoteBibSonomy
Dimension reduction of data sets is a standard problem in the realm of machine learning and knowledge reasoning. They affect patterns in and dependencies on data dimensions and ultimately influence any decision-making processes. Therefore, a wide variety of reduction procedures are in use, each pursuing different objectives. A so far not considered criterion is the conceptual continuity of the reduction mapping, i.e., the preservation of the conceptual structure with respect to the original data set. Based on the notion scale-measure from formal concept analysis we present in this work a) the theoretical foundations to detect and quantify conceptual errors in data scalings; b) an experimental investigation of our approach on eleven data sets that were respectively treated with a variant of non-negative matrix factorization.
@article{hanika2021quantifying,
abstract = {Dimension reduction of data sets is a standard problem in the realm of machine learning and knowledge reasoning. They affect patterns in and dependencies on data dimensions and ultimately influence any decision-making processes. Therefore, a wide variety of reduction procedures are in use, each pursuing different objectives. A so far not considered criterion is the conceptual continuity of the reduction mapping, i.e., the preservation of the conceptual structure with respect to the original data set. Based on the notion scale-measure from formal concept analysis we present in this work a) the theoretical foundations to detect and quantify conceptual errors in data scalings; b) an experimental investigation of our approach on eleven data sets that were respectively treated with a variant of non-negative matrix factorization.},
author = {Hanika, Tom and Hirth, Johannes},
journal = {Accepted for publication at ICCS 2021},
keywords = {closure concept continuous dimension fca myown scale-measure scaling},
note = {cite arxiv:2106.06815, Accepted for publication at ICCS 2021},
title = {Quantifying the Conceptual Error in Dimensionality Reduction},
year = 2021
}
%0 Journal Article
%1 hanika2021quantifying
%A Hanika, Tom
%A Hirth, Johannes
%D 2021
%J Accepted for publication at ICCS 2021
%T Quantifying the Conceptual Error in Dimensionality Reduction
%U http://arxiv.org/abs/2106.06815
%X Dimension reduction of data sets is a standard problem in the realm of machine learning and knowledge reasoning. They affect patterns in and dependencies on data dimensions and ultimately influence any decision-making processes. Therefore, a wide variety of reduction procedures are in use, each pursuing different objectives. A so far not considered criterion is the conceptual continuity of the reduction mapping, i.e., the preservation of the conceptual structure with respect to the original data set. Based on the notion scale-measure from formal concept analysis we present in this work a) the theoretical foundations to detect and quantify conceptual errors in data scalings; b) an experimental investigation of our approach on eleven data sets that were respectively treated with a variant of non-negative matrix factorization.
1.
Hanika, T., Hirth, J.: Knowledge Cores in Large Formal Contexts, http://arxiv.org/abs/2002.11776, (2020).
URLBibTeXEndNoteBibSonomy
Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. 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 interesting patterns. An essentially different approach is used in network science, called \($k$\)-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study \($k$\)-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data sets.
@misc{hanika2020knowledge,
abstract = {Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. 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 interesting patterns. An essentially different approach is used in network science, called \($k$\)-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study \($k$\)-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data sets.},
author = {Hanika, Tom and Hirth, Johannes},
keywords = {bigdata cores fca k-cores lattice myown},
note = {cite arxiv:2002.11776Comment: 13 pages, 10 figures},
title = {Knowledge Cores in Large Formal Contexts},
year = 2020
}
%0 Generic
%1 hanika2020knowledge
%A Hanika, Tom
%A Hirth, Johannes
%D 2020
%T Knowledge Cores in Large Formal Contexts
%U http://arxiv.org/abs/2002.11776
%X Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. 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 interesting patterns. An essentially different approach is used in network science, called \($k$\)-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study \($k$\)-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data sets.
1.
Hanika, T., Hirth, J.: On the Lattice of Conceptual Measurements. (2020).
URLBibTeXEndNoteBibSonomy
We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, and derive a canonical representation. Moreover, we prove said scale-measures are lattice ordered with respect to the closure systems. This enables exploring the set of scale-measures through 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{hanika2020lattice,
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, and derive a canonical representation. Moreover, we prove said scale-measures are lattice ordered with respect to the closure systems. This enables exploring the set of scale-measures through 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},
keywords = {closure-system fca lattice measurement myown scale-measure},
note = {cite arxiv:2012.05267Comment: 19 pages, 6 figures},
title = {On the Lattice of Conceptual Measurements},
year = 2020
}
%0 Journal Article
%1 hanika2020lattice
%A Hanika, Tom
%A Hirth, Johannes
%D 2020
%T On the Lattice of Conceptual Measurements
%U https://arxiv.org/abs/2012.05267
%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, and derive a canonical representation. Moreover, we prove said scale-measures are lattice ordered with respect to the closure systems. This enables exploring the set of scale-measures through 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.
1.
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 = {clojure conexp fca itegpub kde kdepub myown},
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
Talks
- September 2021: ‚Quantifying the Conceptual Error in Dimensionality Reduction‘, Applications of Formal Sciences: Explainable AI, Dagstuhl
- March 2021: ‚Discovery of Conceptual Measurements/Entdecken Begrifflicher Messungen‘, Application of Formal Sciences — Explainable Artificial Intelligence, Dagstuhl
- September 2020: ‚Navigating Conceptual Measurements‘, Dagstuhl Meeting on the Application of Formal Sciences — Knowledge Engineering
Reviewing
- Subreviewer: FCA4AI-2021, 21 August 2021, Montréal, Canada
- Subreviewer: ECML PKDD, 13–17 September 2021
- Subreviewer: 17th Russian Conference on Artificial Intelligence, 21–25 October, 2019, Ulyanovsk, Russia
- Subreviewer: 11th ACM conference on Web Science, June 30–July 3, 2019, Boston, MA, USA
Teaching
Projects
Accompanying my theoretical research, there are two projects I mainly work for:
Conexp-Clj — Scale-Exploration Standalone
Accompanying our research on conceptual measurements, we implemented the exploration of scale-measures algorithm in conexp-cjl and provided a standalone (Ready for Use) version of the algorithm. Associated Papers:
Conexp-Clj — A Research Tool for FCA
The research unit Knowledge & Data Engineering continues the development of the research tool conexp-clj, originally created by Dr. Daniel Borchmann. The continuous enhancement of the software package is supervised by Dr. Tom Hanika. Having such a tool at hand, the research group is able to test and analyze the theoretical research efforts in the realm of formal concept analysis and related fields. The most recent, pre-compiled, release candidate can be downloaded here.
A presentation of the tool can be found in Conexp-Clj – A Research Tool for FCA
BibSonomy
BibSonomy is a scholarly social bookmarking system where researchers manage their collections of publications and web pages. BibSonomy is an open source project, continously developed by researchers in Kassel, Würzburg, and Hanover. Functioning as a test bed for recommendation and ranking algorithms, as well as through the publicly available datasets, containing traces of user behavior on the Web, BibSonomy has been the subject of various scientific studies.
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