Introducer Concepts in n-Dimensional Contexts

TL;DR

This paper generalizes the Galois Sub-Hierarchy, a smaller, manageable structure derived from concept lattices, to n-lattices for multidimensional data, aiding in efficient data analysis.

Contribution

It extends the concept of Galois Sub-Hierarchy to n-lattices, enabling their application to multidimensional data structures.

Findings

Generalization of Galois Sub-Hierarchy to n-lattices

Facilitates efficient analysis of multidimensional data

Reduces complexity of concept structures

Abstract

Concept lattices are well-known conceptual structures that organise interesting patterns-the concepts-extracted from data. In some applications, such as software engineering or data mining, the size of the lattice can be a problem, as it is often too large to be efficiently computed, and too complex to be browsed. For this reason, the Galois Sub-Hierarchy, a restriction of the concept lattice to introducer concepts, has been introduced as a smaller alternative. In this paper, we generalise the Galois Sub-Hierarchy to n-lattices, conceptual structures obtained from multidimensional data in the same way that concept lattices are obtained from binary relations.