activity
20172026
most citedResearch Topic Flows in Co-Authorship Networks

20 citations · 48 across the 24 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.AI2023

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…

cs.AI2023★ 1 cited

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…

cs.LG2023

Selecting Features by their Resilience to the Curse of Dimensionality

Maximilian Stubbemann, Tobias Hille, Tom Hanika

Real-world datasets are often of high dimension and effected by the curse of dimensionality. This hinders their comprehensibility and interpretability. To reduce the complexity fea…

cs.AI2023★ 3 cited

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…

cs.LG2023★ 2 cited

Scaling Dimension

Bernhard Ganter, Tom Hanika, Johannes Hirth

Conceptual Scaling is a useful standard tool in Formal Concept Analysis and beyond. Its mathematical theory, as elaborated in the last chapter of the FCA monograph, still has room…

cs.LG2023★ 11 cited

Conceptual Views on Tree Ensemble Classifiers

Tom Hanika, Johannes Hirth

Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance…