Maximilian Felde

Maximilian Felde Raum 0440 Universität Kassel Fachbereich Elektrotechnik/Informatik Fachgebiet Wissensverarbeitung Wilhelmshöher Allee 73 34121 Kassel Tel.: +49 561 804-6253 Fax.: +49 561 804-6259 Email: felde@cs.uni-kassel.de

Publications

• Interval-Dismantling for Lattices Felde, Maximilian; Koyda, Maren (2022).

  URLBibTeXEndNote

  Dismantling allows for the removal of elements of a set, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique kernel with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.

  @preprint{felde2022intervaldismantling,
  abstract = {Dismantling allows for the removal of elements of a set, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique kernel with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.},
  author = {Felde, Maximilian and Koyda, Maren},
  keywords = {arrow-relations concepts context dismantling fca myown},
  note = {cite arxiv:2208.01479Comment: 12 pages, 5 figures, 1 algorithm},
  title = {Interval-Dismantling for Lattices},
  year = 2022
  }
  

  %0 Generic
  %1 felde2022intervaldismantling
  %A Felde, Maximilian
  %A Koyda, Maren
  %D 2022
  %R 10.48550/arXiv.2208.01479
  %T Interval-Dismantling for Lattices
  %U https://arxiv.org/abs/2208.01479
  %X Dismantling allows for the removal of elements of a set, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique kernel with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.
  

• Attribute Exploration with Multiple Contradicting Partial Experts. Felde, Maximilian; Stumme, Gerd T. Braun, D. Cristea, R. Jäschke (eds.) (2022). 51–65.

  BibTeXEndNote

  Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their shared views. Each expert has their own view of the domain and the views of multiple experts may contain contradicting information.

  @inproceedings{10.1007/978-3-031-16663-1_5,
  abstract = {Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their shared views. Each expert has their own view of the domain and the views of multiple experts may contain contradicting information.},
  address = {Cham},
  author = {Felde, Maximilian and Stumme, Gerd},
  booktitle = {Graph-Based Representation and Reasoning},
  editor = {Braun, Tanya and Cristea, Diana and Jäschke, Robert},
  keywords = {attribute-exploration fca multiple-experts myown},
  pages = {51--65},
  publisher = {Springer International Publishing},
  title = {Attribute Exploration with Multiple Contradicting Partial Experts},
  year = 2022
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-16663-1_5
  %A Felde, Maximilian
  %A Stumme, Gerd
  %B Graph-Based Representation and Reasoning
  %C Cham
  %D 2022
  %E Braun, Tanya
  %E Cristea, Diana
  %E Jäschke, Robert
  %I Springer International Publishing
  %P 51--65
  %R 10.1007/978-3-031-16663-1_5
  %T Attribute Exploration with Multiple Contradicting Partial Experts
  %X Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their shared views. Each expert has their own view of the domain and the views of multiple experts may contain contradicting information.
  %@ 978-3-031-16663-1
  

• Triadic Exploration and Exploration with Multiple Experts. Felde, Maximilian; Stumme, Gerd A. Braud, A. Buzmakov, T. Hanika, F. Le Ber (eds.) (2021). 175–191.

  BibTeXEndNote

  Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.

  @inproceedings{10.1007/978-3-030-77867-5_11,
  abstract = {Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.},
  address = {Cham},
  author = {Felde, Maximilian and Stumme, Gerd},
  booktitle = {Formal Concept Analysis},
  editor = {Braud, Agnès and Buzmakov, Aleksey and Hanika, Tom and Le Ber, Florence},
  keywords = {2021 attribute-exploration fca myown},
  pages = {175--191},
  publisher = {Springer International Publishing},
  title = {Triadic Exploration and Exploration with Multiple Experts},
  year = 2021
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-030-77867-5_11
  %A Felde, Maximilian
  %A Stumme, Gerd
  %B Formal Concept Analysis
  %C Cham
  %D 2021
  %E Braud, Agnès
  %E Buzmakov, Aleksey
  %E Hanika, Tom
  %E Le Ber, Florence
  %I Springer International Publishing
  %P 175--191
  %R 10.1007/978-3-030-77867-5_11
  %T Triadic Exploration and Exploration with Multiple Experts
  %X Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.
  %@ 978-3-030-77867-5
  

• Null Models for Formal Contexts. Felde, Maximilian; Hanika, Tom; Stumme, Gerd (2020). 11(3) 135.

  URLBibTeXEndNote

  @article{felde2020null,
  author = {Felde, Maximilian and Hanika, Tom and Stumme, Gerd},
  journal = {Information},
  keywords = {2020 coll-exploration-paper context fca myown null-model},
  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
  

• Interactive Collaborative Exploration using Incomplete Contexts. Felde, Maximilian; Stumme, Gerd (2019).

  URLBibTeXEndNote

  A common representation of information about relations of objects and attributes in knowledge domains are data-tables. The structure of such information can be analysed using Formal Concept Analysis (FCA). Attribute exploration is a knowledge acquisition method from FCA that reveals dependencies in a set of attributes with help of a domain expert. However, in general no single expert is capable (time- and knowledge-wise) of exploring knowledge domains alone. Therefore it is important to develop methods that allow multiple experts to explore domains together. To this end we build upon results on representation of incomplete knowledge [2, 8-10], adapt the corresponding version of attribute exploration to fit the setting of multiple experts and suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. Furthermore we discuss ways of comparing collaboration strategies and suggest avenues for future research.

  @misc{felde2019interactive,
  abstract = {A common representation of information about relations of objects and attributes in knowledge domains are data-tables. The structure of such information can be analysed using Formal Concept Analysis (FCA). Attribute exploration is a knowledge acquisition method from FCA that reveals dependencies in a set of attributes with help of a domain expert. However, in general no single expert is capable (time- and knowledge-wise) of exploring knowledge domains alone. Therefore it is important to develop methods that allow multiple experts to explore domains together. To this end we build upon results on representation of incomplete knowledge [2, 8-10], adapt the corresponding version of attribute exploration to fit the setting of multiple experts and suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. Furthermore we discuss ways of comparing collaboration strategies and suggest avenues for future research.},
  author = {Felde, Maximilian and Stumme, Gerd},
  keywords = {attribute-exploration fca incomplete-context myown},
  title = {Interactive Collaborative Exploration using Incomplete Contexts},
  year = 2019
  }
  

  %0 Generic
  %1 felde2019interactive
  %A Felde, Maximilian
  %A Stumme, Gerd
  %D 2019
  %T Interactive Collaborative Exploration using Incomplete Contexts
  %U https://arxiv.org/abs/1908.08740
  %X A common representation of information about relations of objects and attributes in knowledge domains are data-tables. The structure of such information can be analysed using Formal Concept Analysis (FCA). Attribute exploration is a knowledge acquisition method from FCA that reveals dependencies in a set of attributes with help of a domain expert. However, in general no single expert is capable (time- and knowledge-wise) of exploring knowledge domains alone. Therefore it is important to develop methods that allow multiple experts to explore domains together. To this end we build upon results on representation of incomplete knowledge [2, 8-10], adapt the corresponding version of attribute exploration to fit the setting of multiple experts and suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. Furthermore we discuss ways of comparing collaboration strategies and suggest avenues for future research.
  

• Formal Context Generation Using Dirichlet Distributions. Felde, Maximilian; Hanika, Tom in Lecture Notes in Computer Science, D. Endres, M. Alam, D. Sotropa (eds.) (2019). (Vol. 11530) 57–71.

  URLBibTeXEndNote

  @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 = {2019 fca itegpub kde kdepub myown publist},
  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
  %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
  

Selected Activities

• Subreviewer: 15th International Conference on Formal Concept Analysis (ICFCA 2019), June 25 – 28, Frankfurt, Germany
• Subreviewer: 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), August 4 – 8, 2019, Anchorage, Alaska, USA
• Subreviewer: 24th European Conference on Artificial Intelligence (ECAI 2020), August 29 – September 2, Santiago de Compostela, Spain
• Subreviewer: 19th International Semantic Web Conference (ISWC 2020), November 1 – 6, Athens, Greece Virtual Conference
• Subreviewer: The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2020), December 7 – 10, The Hague, Netherlands Virtual Conference
• Subreviewer: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2021), September 13 – 17, Virtual Conference

Teaching

• Summer Semester 2021:
  • Übung – Conceptual Data Analysis
• Winter Semester 2020/2021:
  • Übung – Knowledge Discovery
  • Seminar – Ausgewählte Themen der Wissensverarbeitung
• Summer Semester 2020:
  • Labor – Qualitative Datenanalyse
• Winter Semester 2019/2020:
  • Übung – Knowledge Discovery in Databases
• Summer Semester 2019:
  • Übung – Datenbanken
• Winter Semester 2018/2019:
  • Labor – Social Network Analysis
• Summer Semester 2018:
  • Übung – Datenbanken