MASS-CSP: Mining with Answer Set Solving for Contrast Sequential Pattern Mining
Abstract
Abstract In this paper, we present MASS-CSP (Mining with Answer Set Solving - Contrast Sequential Patterns), a declarative approach to the Contrast Sequential Pattern Mining (CSPM) task, which is based on the logic-based framework of Answer Set Programming (ASP). The CSPM task focuses on identifying significant differences in frequent sequences relative to specific classes, leading to the concept of a contrast sequential pattern. The article describes how MASS-CSP addresses the CSPM task and related extensions-mining closed, maximal and constrained patterns. Evaluation aims at comparing the basic version of MASS-CSP against the extended versions as regards the size of output and time-memory requirements.
Cite
Text
Sterlicchio and Lisi. "MASS-CSP: Mining with Answer Set Solving for Contrast Sequential Pattern Mining." Machine Learning, 2025. doi:10.1007/S10994-025-06876-0Markdown
[Sterlicchio and Lisi. "MASS-CSP: Mining with Answer Set Solving for Contrast Sequential Pattern Mining." Machine Learning, 2025.](https://mlanthology.org/mlj/2025/sterlicchio2025mlj-masscsp/) doi:10.1007/S10994-025-06876-0BibTeX
@article{sterlicchio2025mlj-masscsp,
title = {{MASS-CSP: Mining with Answer Set Solving for Contrast Sequential Pattern Mining}},
author = {Sterlicchio, Gioacchino and Lisi, Francesca Alessandra},
journal = {Machine Learning},
year = {2025},
pages = {235},
doi = {10.1007/S10994-025-06876-0},
volume = {114},
url = {https://mlanthology.org/mlj/2025/sterlicchio2025mlj-masscsp/}
}