AINeutralarXiv – CS AI · Apr 156/10
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Enhancing Clustering: An Explainable Approach via Filtered Patterns
Researchers propose a pattern reduction framework for explainable clustering that eliminates redundant k-relaxed frequent patterns (k-RFPs) while maintaining cluster quality. The approach uses formal characterization and optimization strategies to reduce computational complexity in knowledge-driven unsupervised learning systems.