A Relaxation-Based Approach for Mining Diverse Closed Patterns
Abstract
In recent years, pattern mining has moved from a slow-moving repeated three-step process to a much more agile iterative/user-centric mining model. A vital ingredient of this framework is the ability to quickly present a set of diverse patterns to the user. In this paper, we use constraint programming (well-suited to user-centric mining due to its rich constraint language) to efficiently mine a diverse set of closed patterns. Diversity is controlled through a threshold on the Jaccard similarity of pattern occurrences. We show that the Jaccard measure has no monotonicity property, which prevents usual pruning techniques and makes classical pattern mining unworkable. This is why we propose anti-monotonic lower and upper bound relaxations, which allow effective pruning, with an efficient branching rule, boosting the whole search process. We show experimentally that our approach significantly reduces the number of patterns and is very efficient in terms of running times, particularly on dense data sets.
Cite
Text
Hien et al. "A Relaxation-Based Approach for Mining Diverse Closed Patterns." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67658-2_3Markdown
[Hien et al. "A Relaxation-Based Approach for Mining Diverse Closed Patterns." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/hien2020ecmlpkdd-relaxationbased/) doi:10.1007/978-3-030-67658-2_3BibTeX
@inproceedings{hien2020ecmlpkdd-relaxationbased,
title = {{A Relaxation-Based Approach for Mining Diverse Closed Patterns}},
author = {Hien, Arnold and Loudni, Samir and Aribi, Noureddine and Lebbah, Yahia and Laghzaoui, Mohammed El Amine and Ouali, Abdelkader and Zimmermann, Albrecht},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2020},
pages = {36-54},
doi = {10.1007/978-3-030-67658-2_3},
url = {https://mlanthology.org/ecmlpkdd/2020/hien2020ecmlpkdd-relaxationbased/}
}