papers

Publications (44)

cs.AI2023

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.AI2021

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…

cs.AI2020

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…

cs.AI2020

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…

cs.LG2025

STADE: Standard Deviation as a Pruning Metric

Diego Coello de Portugal Mecke, Haya Alyoussef, Maximilian Stubbemann +3

Recently, Large Language Models (LLMs) have become very widespread and are used to solve a wide variety of tasks. To successfully handle these tasks, LLMs require longer training t…

cs.AI2018

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…