20 citations · 158 across the 42 of their papers we have counts for
19 papers · 1 filter
MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off
Ibram Abdelmalak, Mischa Putzke, Jungmin Choi +3
Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about the future of another. Yet the…
A Repository for Formal Contexts
Tom Hanika, Robert Jäschke
Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of…
The Geometric Structure of Topic Models
Johannes Hirth, Tom Hanika
Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics.…
Towards Ordinal Data Science
Gerd Stumme, Dominik Dürrschnabel, Tom Hanika
Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amo…
Automatic Textual Explanations of Concept Lattices
Johannes Hirth, Viktoria Horn, Gerd Stumme +1
Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such…
Ordinal Motifs in Lattices
Johannes Hirth, Viktoria Horn, Gerd Stumme +1
Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of…
Discovering Locally Maximal Bipartite Subgraphs
Dominik Dürrschnabel, Tom Hanika, Gerd Stumme
Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs. Yet, discovering them in large graphs is known to be computationally…
Exploring Scale-Measures of Data Sets
Tom Hanika, Johannes Hirth
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 siz…
On the Lattice of Conceptual Measurements
Tom Hanika, Johannes Hirth
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 represe…
Knowledge Cores in Large Formal Contexts
Tom Hanika, Johannes Hirth
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 essent…
Orometric Methods in Bounded Metric Data
Maximilian Stubbemann, Tom Hanika, Gerd Stumme
A large amount of data accommodated in knowledge graphs (KG) is actually metric. For example, the Wikidata KG contains a plenitude of metric facts about geographic entities like ci…
Discovering Implicational Knowledge in Wikidata
Tom Hanika, Maximilian Marx, Gerd Stumme
Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Examples include the proprietary knowledge graphs o…
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…
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…
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…
Towards Collaborative Conceptual Exploration
Tom Hanika, Jens Zumbrägel
In domains with high knowledge distribution a natural objective is to create principle foundations for collaborative interactive learning environments. We present a first mathemati…
On the Usability of Probably Approximately Correct Implication Bases
Daniel Borchmann, Tom Hanika, Sergei Obiedkov
We revisit the notion of probably approximately correct implication bases from the literature and present a first formulation in the language of formal concept analysis, with the g…