MASS-CSP: mining with answer set solving for contrast sequential pattern mining

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 signific...

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Veröffentlicht in:Machine learning Jg. 114; H. 11; S. 235
Hauptverfasser: Sterlicchio, Gioacchino, Lisi, Francesca Alessandra
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2025
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computer Science
Control
Data mining
Datasets
Declarative programming
Efficiency
Logic programming
Machine Learning
Mathematical programming
Mechatronics
Natural Language Processing (NLP)
Pattern analysis
Programming languages
Robotics
Semantic web
Simulation and Modeling
Syntax
Declarative pattern mining
Answer set programming
Contrast sequential pattern mining
ISSN:0885-6125, 1573-0565
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-025-06876-0