ACMKC: A Compact Associative Classification Model Using K-Modes Clustering with Rule Representations by Coverage

Jamolbek Mattiev (), Monte Davityan and Branko Kavsek
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Jamolbek Mattiev: Computer Science Department, Urgench State University, Khamid Alimdjan 14, Urgench 220100, Uzbekistan
Monte Davityan: Computer Science Department, California State University of Fullerton, 2555 Nutwood Avenue, Fullerton, CA 92831, USA
Branko Kavsek: Department of Information Sciences and Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia

Mathematics, 2023, vol. 11, issue 18, 1-14

Abstract: The generation and analysis of vast amounts of data have become increasingly prevalent in diverse applications. In this study, we propose a novel approach to address the challenge of rule explosion in association rule mining by utilizing the coverage-based representations of clusters determined by K-modes. We utilize the FP-Growth algorithm to generate class association rules (CARs). To further enhance the interpretability and compactness of the rule set, we employ the K-modes clustering algorithm with a distance metric that binarizes the rules. The optimal number of clusters is determined using the silhouette score. Representative rules are then selected based on their coverage within each cluster. To evaluate the effectiveness of our approach, we conducted experimental evaluations on both UCI and Kaggle datasets. The results demonstrate a significant reduction in the rule space (71 rules on average, which is the best result among all state-of-the-art rule-learning algorithms), aligning with our goal of producing compact classifiers. Our approach offers a promising solution for managing rule complexity in association rule mining, thereby facilitating improved rule interpretation and analysis, while maintaining a significantly similar classification accuracy (ACMKC: 80.0% on average) to other rule learners on most of the datasets.

Keywords: class association rules; clustering; representative rule; model coverage; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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