# Relevant Attributes in Formal Contexts

## TL;DR

This paper introduces a novel attribute selection method for formal concept analysis, using relevance functions and entropy-based approximations to handle large data sets efficiently.

## Contribution

It proposes the concept of relevant attributes and a relevance function, addressing computational challenges with an entropy-based approximation approach.

## Key findings

- Defined relevant attributes and relevance function
- Developed an entropy-based approximation method
- Enhanced scalability for large formal contexts

## Abstract

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 sampling, parallelization, or attribute extraction. A so far not investigated method in the realm of formal concept analysis is attribute selection, as done in machine learning. Building up on this we introduce a method for attribute selection in formal contexts. To this end, we propose the notion of relevant attributes which enables us to define a relative relevance function, reflecting both the order structure of the concept lattice as well as distribution of objects on it. Finally, we overcome computational challenges for computing the relative relevance through an approximation approach based on information entropy.