Fast Redescription Mining Using Locality-Sensitive Hashing

TL;DR

This paper introduces new algorithms for redescription mining that leverage locality-sensitive hashing to significantly improve efficiency, especially with large datasets containing many numerical attributes.

Contribution

The authors develop novel algorithms that drastically speed up redescription mining by applying locality-sensitive hashing and tailored discretisation of numerical data.

Findings

Algorithms are orders of magnitude faster than existing methods.

Effective handling of numerical attributes through tailored discretisation.

Applicable to large datasets with many numerical features.

Abstract

Redescription mining is a data analysis technique that has found applications in diverse fields. The most used redescription mining approaches involve two phases: finding matching pairs among data attributes and extending the pairs. This process is relatively efficient when the number of attributes remains limited and when the attributes are Boolean, but becomes almost intractable when the data consist of many numerical attributes. In this paper, we present new algorithms that perform the matching and extension orders of magnitude faster than the existing approaches. Our algorithms are based on locality-sensitive hashing with a tailored approach to handle the discretisation of numerical attributes as used in redescription mining.