Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data
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arXiv
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| Format: | Preprint |
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2026
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| author | Li, Lehao Huang, Qiang Ang, Yihao Low, Bryan Kian Hsiang Tung, Anthony K. H. Xiao, Xiaokui |
| author_facet | Li, Lehao Huang, Qiang Ang, Yihao Low, Bryan Kian Hsiang Tung, Anthony K. H. Xiao, Xiaokui |
| contents | 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. WISE introduces Binary Encoding with Padding (BEP) to align heterogeneous features in a unified sparse space, a Leave-One-Feature-Out (LOFO) strategy to sense multiple high-quality and diverse feature-weighting views, and a two-stage weight-aware clustering procedure to aggregate alternative semantic partitions. To ensure intrinsic interpretability, we further develop Discriminative FreqItems (DFI), which yields feature-level explanations that are consistent from instances to clusters with an additive decomposition guarantee. Extensive experiments on six real-world datasets demonstrate that WISE consistently outperforms classical and neural baselines in clustering quality while remaining efficient, and produces faithful, human-interpretable explanations grounded in the same primitives that drive clustering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05857 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data Li, Lehao Huang, Qiang Ang, Yihao Low, Bryan Kian Hsiang Tung, Anthony K. H. Xiao, Xiaokui Machine Learning 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. WISE introduces Binary Encoding with Padding (BEP) to align heterogeneous features in a unified sparse space, a Leave-One-Feature-Out (LOFO) strategy to sense multiple high-quality and diverse feature-weighting views, and a two-stage weight-aware clustering procedure to aggregate alternative semantic partitions. To ensure intrinsic interpretability, we further develop Discriminative FreqItems (DFI), which yields feature-level explanations that are consistent from instances to clusters with an additive decomposition guarantee. Extensive experiments on six real-world datasets demonstrate that WISE consistently outperforms classical and neural baselines in clustering quality while remaining efficient, and produces faithful, human-interpretable explanations grounded in the same primitives that drive clustering. |
| title | Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.05857 |