Clustering-Guided Knowledge Base for Multi-Objective Rule Mining on Imbalanced Datasets
TL;DR
A clustering-based knowledge base is proposed that captures positive-class structure before search begins and uses it to focus both initialization and neighborhood exploration in Moca-I, a multi-objective local-search algorithm that mines interpretable rule sets by trading off minority-class recall, precision, and complexity.
Abstract
Mining classification rules for the minority class is challenging not only because positive examples are rare, but because they concentrate in small, geometrically irregular subregions of feature space that unguided search methods systematically miss. We propose a clustering-based knowledge base that captures positive-class structure before search begins and uses it to focus both initialization and neighborhood exploration in Moca-I, a multi-objective local-search algorithm that mines interpretable rule sets by trading off minority-class recall, precision, and complexity. Rather than sampling attribute conditions blindly from the full discretized space, the knowledge base clusters positive-class instances, extracts per-cluster attribute ranges, and aligns them with the algorithm's discretization—seeding the initial archive with minority-class-informed prototypes and restricting neighborhood operators to locally relevant regions. We instantiate this framework with two clustering methods: Self-Organizing Maps (MOCA-ISOM), which preserve the topological structure of the positive-class manifold, and K-Means (MOCA-IKM), a centroid-based baseline. Evaluated on 19 imbalanced benchmark datasets, MOCA-ISOM achieves statistically significant F-measure improvements on eight datasets and Hypervolume improvements on nine, with gains up to +17% absolute on high-dimensional data. MOCA-IKM shows comparable average performance but exhibits five statistically significant degradations.