in reversed chronological order


  1. ordinal-motifs.png
    Johannes Hirth, Viktoria Horn, Gerd Stumme, Tom Hanika
    Ordinal motifs in lattices
    Information Sciences 659 (2024), 120009.
  2. divergent.png
    Katharina B. Budde, Christian Rellstab, Myriam Heuertz, Felix Gugerli, Tom Hanika, Miguel Verdú, Juli G. Pausas, Santiago C. González-Martínez
    Divergent selection in a Mediterranean pine on local spatial scales
    Journal of Ecology 112 (2024), no. 2, 278-290.


  1. tmlr.png
    Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
    Intrinsic Dimension for Large-Scale Geometric Learning
    Transactions on Machine Learning Research (2023).
  2. topic-flow.png
    Bastian Schäfermeier, Johannes Hirth, Tom Hanika
    Research Topic Flows in Co-Authorship Networks
    Scientometrics 128 (2023), no. 9, 5051–5078.
  3. scaling-dim.png
    Bernhard Ganter, Tom Hanika, Johannes Hirth
    Scaling Dimension
    In: 17th ICFCA, Proceedings, D. Dürrschnabel, D. López-Rodríguez (eds.), 64–77. Springer (2023).
  4. automatic-explanations.png
    Johannes Hirth, Viktoria Horn, Gerd Stumme, Tom Hanika
    Automatic Textual Explanations of Concept Lattices
    In: Graph-Based Representation and Reasoning, ICCS 2023, M. Ojeda-Aciego, K. Sauerwald, R. Jäschke (eds.), 138–152. Springer (2023).
  5. conview-trees.png
    Tom Hanika, Johannes Hirth
    Conceptual views on tree ensemble classifiers
    International Journal of Approximate Reasoning 159 (2023), 108930.
  6. drawing.png
    Dominik Dürrschnabel, Tom Hanika, Gerd Stumme
    Drawing Order Diagrams Through Two-Dimension Extension
    J. Graph Algorithms Appl. 27 (2023), no. 9, 783–802.
  7. tods.png
    Gerd Stumme, Dominik Dürrschnabel, Tom Hanika
    Towards Ordinal Data Science
    Transactions on Graph Data and Knowledge 1 (2023), no. 1, 6:1–6:39.


  1. id.png
    Tom Hanika, Friedrich Martin Schneider, Gerd Stumme
    Intrinsic dimension of geometric data sets
    Tohoku Mathematical Journal 74 (2022), no. 1, 23 – 52.
  2. fca2vec.png
    Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann
    FCA2VEC: Embedding Techniques for Formal Concept Analysis
    In: Complex Data Analytics with FCA, R. Missaoui, L. Kwuida, T. Abdessalem (eds.), 47–74. Springer International Publishing (2022).
  3. cores.png
    Tom Hanika, Johannes Hirth
    Knowledge Cores in Large Formal Contexts
    Ann Math Artif Intell 90 (2022), no. 6, 537–567.
  4. concept-measurement.png
    Tom Hanika, Johannes Hirth
    On the lattice of conceptual measurements
    Information Sciences 613 (2022), 453-468.


  1. proximity.png
    Tobias Koopmann, Maximilian Stubbemann, Matthias Kapa, Michael Paris, Guido Buenstorf, Tom Hanika, Andreas Hotho, Robert Jäschke, Gerd Stumme
    Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research
    Scientometrics 126 (2021), 9847–9868.
  2. tps.png
    Bastian Schaefermeier, Gerd Stumme, Tom Hanika
    Topic space trajectories
    Scientometrics 126 (2021), no. 7, 5759–5795.
  3. expscale.png
    Tom Hanika, Johannes Hirth
    Exploring Scale-Measures of Data Sets
    In: 16th ICFCA, Proceedings, A. Braud, A. Buzmakov, T. Hanika, F. Le Ber (eds.), 261–269. Springer (2021).
  4. icfca2021.png
    Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings
    A. Braud, A. Buzmakov, T. Hanika, F. Ber (eds.), Springer (2021).
  5. quantierror.png
    Tom Hanika, Johannes Hirth
    Quantifying the Conceptual Error in Dimensionality Reduction
    In: Graph-Based Representation and Reasoning - 26th ICCS 2021, T. Braun, M. Gehrke, T. Hanika, N. Hernandez (eds.), 105–118. Springer (2021).
  6. iccs2021.png
    Graph-Based Representation and Reasoning - 26th Int. Conf. on Conceptual Structures, ICCS 2021, Proceedings
    T. Braun, M. Gehrke, T. Hanika, N. Hernandez (eds.), Springer (2021).


  1. pacexp.png
    Daniel Borchmann, Tom Hanika, Sergei Obiedkov
    Probably approximately correct learning of Horn envelopes from queries
    Discrete Applied Mathematics 273 (2020), 30 - 42.
  2. orometric.png
    Maximilian Stubbemann, Tom Hanika, Gerd Stumme
    Orometric Methods in Bounded Metric Data
    In: 18th Int. Symposium on Intelligent Data Analysis, IDA Proceedings, M. Berthold, A. Feelders, G. Krempl (eds.), 496–508. Springer (2020).
  3. nullmodel.png
    Maximilian Felde, Tom Hanika, Gerd Stumme
    Null Models for Formal Contexts
    Information 11 (2020), no. 3, 135.


  1. wd.png
    T. Hanika, M. Marx, G. Stumme
    Discovering Implicational Knowledge in Wikidata
    In: Formal Concept Analysis - 15th Int. Conf., ICFCA 2019, Proceedings, D. Cristea, F. Le Ber, B. Sertkaya (eds.), 315–323. Springer (2019).
  2. dirichlet.png
    Maximilian Felde, Tom Hanika
    Formal Context Generation Using Dirichlet Distributions
    In: 24th Int. Conf. on Conceptual Structures, ICCS 2019, Proceedings, (eds.), 57–71. (2019).
  3. rel.png
    Tom Hanika, Maren Koyda, Gerd Stumme
    Relevant Attributes in Formal Contexts
    In: Graph-Based Representation and Reasoning, ICCS 2019, Proceedings, (eds.), 102–116. (2019).
  4. dimdrawsup.png
    Dominik Dürrschnabel, Tom Hanika, Gerd Stumme
    DimDraw - A Novel Tool for Drawing Concept Lattices
    In: Sup. Proceedings of ICFCA 2019 Conf., D. Cristea, F. Ber, R. Missaoui, L. Kwuida, B. Sertkaya (eds.), 60–64. (2019).
  5. distances.png
    Bastian Schaefermeier, Tom Hanika, Gerd Stumme
    Distances for wifi based topological indoor mapping
    In: Proceedings of the 16th EAI Int. Conf. on Mobile and Ubiquitous Systems: Computing, Networking and Services, (eds.), . ACM (2019).
  6. bwpraxis.png
    Tom Hanika, Mark Kibanov, Jonathan Kropf, Stefan Laser
    Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt populär und besonders interessant für die Forschung geworden ist.
    In: Digitale Bewertungspraktiken, J. Kropf, S. Laser (eds.), 165–188. Springer (2019).
  7. conexp-sup.png
    Tom Hanika, Johannes Hirth
    Conexp-Clj - A Research Tool for FCA
    In: ICFCA (Suppl.), D. Cristea, F. Ber, R. Missaoui, L. Kwuida, B. Sertkaya (eds.), 70-75. CEUR (2019).
  8. Tom Hanika
    Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis
    University of Kassel, Germany (2019).


  1. clones.png
    S. Doerfel, T. Hanika, G. Stumme
    Clones in Graphs
    In: Foundations of Intelligent Systems - 24th Int. Symposium, ISMIS 2018, M. Ceci, N. Japkowicz, J. Liu, G. Papadopoulos, Z. Ras (eds.), 56-66. Springer (2018).
  2. cil.png
    Tom Hanika, Jens Zumbrägel
    Towards Collaborative Conceptual Exploration
    In: Graph-Based Representation and Reasoning, ICCS 2018, Proceedings, P. Chapman, D. Endres, N. Pernelle (eds.), 120-134. Springer (2018).


  1. individ.png
    D. Borchmann, T. Hanika
    Individuality in Social Networks
    In: Formal Concept Analysis of Social Networks, R. Missaoui, S. Kuznetsov, S. Obiedkov (eds.), 19–40. Springer (2017).
  2. pac.png
    Daniel Borchmann, Tom Hanika, Sergei Obiedkov
    On the Usability of Probably Approximately Correct Implication Bases.
    In: Formal Concept Analysis - 14th Int. Conf., ICFCA 2017, Proceedings, K. Bertet, D. Borchmann, P. Cellier, S. Ferré (eds.), 72-88. Springer (2017).


  1. Martin Atzmueller, Tom Hanika, Gerd Stumme, Richard Schaller, Bernd Ludwig
    Social event network analysis: Structure, preferences, and reality
    In: 2016 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining, R. Kumar, J. Caverlee, H. Tong (eds.), 613–620. IEEE Computer Society (2016).
  2. stego.png
    Daniel Borchmann, Tom Hanika
    Some Experimental Results on Randomly Generating Formal Contexts
    In: 13th Int. Conf. on Concept Lattices and Their Applications, M. Huchard, S. Kuznetsov (eds.), 57–69. (2016).