AI RESEARCH
Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data
arXiv CS.LG
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ArXi:2604.05857v1 Announce Type: new Clustering mixed-type tabular data is fundamental for exploratory analysis, yet remains challenging due to misaligned numerical-categorical representations, uneven and context-dependent feature relevance, and disconnected and post-hoc explanation from the clustering process. We propose WISE, a Weight-Informed Self-Explaining framework that unifies representation, feature weighting, clustering, and interpretation in a fully unsupervised and transparent pipeline.