# Knowledge Cores in Large Formal Contexts

arXiv: https://arxiv.org/abs/2002.11776 · HTML: https://mlsanity.com/paper/2002.11776/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [Attribute Selection using Contranominal Scales](https://mlsanity.com/paper/2106.10978/) (2021)
- [Conceptual Views on Tree Ensemble Classifiers](https://mlsanity.com/paper/2302.05270/) (2023)
- [Exploring Scale-Measures of Data Sets](https://mlsanity.com/paper/2102.02576/) (2021)
- [Ordinal Motifs in Lattices](https://mlsanity.com/paper/2304.04827/) (2023)
- [Automatic Textual Explanations of Concept Lattices](https://mlsanity.com/paper/2304.08093/) (2023)
- [Towards Ordinal Data Science](https://mlsanity.com/paper/2307.09477/) (2023)
- [The Geometric Structure of Topic Models](https://mlsanity.com/paper/2403.03607/) (2024)

## Abstract (arXiv)

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 essential to come up with techniques to cope with this problem. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and interesting patterns. An essentially different approach is used in network science, called k-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data sets.