# Fast Redescription Mining Using Locality-Sensitive Hashing

arXiv: https://arxiv.org/abs/2406.04148 · HTML: https://mlsanity.com/paper/2406.04148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Locality Sensitive Hashing with Extended Differential Privacy](https://mlsanity.com/paper/2010.09393/) (2020)
- [A framework for redescription set construction](https://mlsanity.com/paper/1606.03935/) (2016)
- [Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients](https://mlsanity.com/paper/1702.06831/) (2017)
- [Differentially Private Tree-Based Redescription Mining](https://mlsanity.com/paper/2212.06630/) (2022)

## Abstract (arXiv)

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.