publications

List of Publications and Preprints by Tom Hanika

2019

• Stubbemann, M., Hanika, T., Stumme, G.: Orometric Methods in Bounded Metric Data. CoRR. abs/1907.09239, (2019).
• Hanika, T., Kibanov, M., Kropf, J., Laser, S.: Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt populär und besonders interessant für die Forschung geworden ist. In: Kropf, J. and Laser, S. (eds.) Digitale Bewertungspraktiken. p. 165--188. Springer (2019).
• 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).
• 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).
• Dürrschnabel, D., Hanika, T., Stumme, G.: Drawing Order Diagrams Through Two-Dimension Extension, http://arxiv.org/abs/1906.06208, (2019).

  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.) ICFCA. pp. 315-323. Springer (2019).
• 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).
• 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).

2018

• Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic dimension and its application to association rules. CoRR. abs/1805.05714, (2018).

  The curse of dimensionality in the realm of association rules is twofold. Firstly, we have the well known exponential increase in computational complexity with increasing item set size. Secondly, there is a emphrelated curse concerned with the distribution of (spare) data itself in high dimension. The former problem is often coped with by projection, i.e., feature selection, whereas the best known strategy for the latter is avoidance. This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures. This recent development enables the application of various other methods from geometric analysis to be investigated and applied in machine learning procedures in the presence of high dimension.
• Hanika, T., Zumbrägel, J.: Towards Collaborative Conceptual Exploration. In: Chapman, P., Endres, D., and Pernelle, N. (eds.) ICCS. pp. 120-134. Springer (2018).
• Borchmann, D., Hanika, T., Obiedkov, S.: Probably approximately correct learning of Horn envelopes from queries. CoRR. abs/1807.06149, (2018).

  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.
• 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).
• Schäfermeier, B., Hanika, T., Stumme, G.: Distances for WiFi Based Topological Indoor Mapping, http://arxiv.org/abs/1809.07405, (2018).

  For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.
• Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic Dimension of Geometric Data Sets, http://arxiv.org/abs/1801.07985, (2018).

  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 mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
• Hanika, T., Koyda, M., Stumme, G.: Relevant Attributes in Formal Contexts. CoRR. abs/1812.08868, (2018).

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. p. 19--40. Springer International Publishing, Cham (2017).

  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.
• 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.) ICFCA. pp. 72-88. Springer (2017).

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).
• 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).