Fast Redescription Mining Using Locality-Sensitive Hashing
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.
Cite
Text
Karjalainen et al. "Fast Redescription Mining Using Locality-Sensitive Hashing." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70368-3_8Markdown
[Karjalainen et al. "Fast Redescription Mining Using Locality-Sensitive Hashing." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/karjalainen2024ecmlpkdd-fast/) doi:10.1007/978-3-031-70368-3_8BibTeX
@inproceedings{karjalainen2024ecmlpkdd-fast,
title = {{Fast Redescription Mining Using Locality-Sensitive Hashing}},
author = {Karjalainen, Maiju and Galbrun, Esther and Miettinen, Pauli},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2024},
pages = {124-142},
doi = {10.1007/978-3-031-70368-3_8},
url = {https://mlanthology.org/ecmlpkdd/2024/karjalainen2024ecmlpkdd-fast/}
}