20 citations · 158 across the 42 of their papers we have counts for
7 papers · 1 filter
Relevant Attributes in Formal Contexts
Tom Hanika, Maren Koyda, Gerd Stumme
Computing conceptual structures, like formal concept lattices, is in the age of massive data sets a challenging task. There are various approaches to deal with this, e.g., random s…
Formal Context Generation using Dirichlet Distributions
Maximilian Felde, Tom Hanika
We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions. For this purpose we investigate the predominant way to generate formal contexts, a…
Distances for WiFi Based Topological Indoor Mapping
Bastian Schäfermeier, Tom Hanika, Gerd Stumme
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…
Probably approximately correct learning of Horn envelopes from queries
Daniel Borchmann, Tom Hanika, Sergei Obiedkov
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. Attrib…
Intrinsic dimension and its application to association rules
Tom Hanika, Friedrich Martin Schneider, Gerd Stumme
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…
Clones in Graphs
Stephan Doerfel, Tom Hanika, Gerd Stumme
Finding structural similarities in graph data, like social networks, is a far-ranging task in data mining and knowledge discovery. A (conceptually) simple reduction would be to com…
Intrinsic Dimension of Geometric Data Sets
Tom Hanika, Friedrich Martin Schneider, Gerd Stumme
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…