On interestingness measures of formal concepts

TL;DR

This paper reviews and compares various interestingness measures for formal concepts and closed itemsets, focusing on their computational efficiency, robustness to noise, and effectiveness in ranking, to aid knowledge discovery.

Contribution

It provides a comparative analysis of interestingness measures for formal concepts, highlighting their computational and practical differences for data mining applications.

Findings

Identifies measures that are computationally efficient

Evaluates measures' robustness to noisy data

Analyzes correlation in concept ranking results

Abstract

Formal concepts and closed itemsets proved to be of big importance for knowledge discovery, both as a tool for concise representation of association rules and a tool for clustering and constructing domain taxonomies and ontologies. Exponential explosion makes it difficult to consider the whole concept lattice arising from data, one needs to select most useful and interesting concepts. In this paper interestingness measures of concepts are considered and compared with respect to various aspects, such as efficiency of computation and applicability to noisy data and performing ranking correlation.