Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data

• URL: http://arxiv.org/abs/2604.05857v1
• Date: Tue, 07 Apr 2026 13:18:31 GMT
• Title: Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data
• Abstract summary: WISE is a framework that unifies representation, feature weighting, clustering, and interpretation.<n>It produces faithful, human-interpretable explanations grounded in the same primitives that drive clustering.
• Score: 63.62853416081748
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Enhancing Clustering: An Explainable Approach via Filtered Patterns [4.576379639081977]
  explainable clustering is a knowledge-driven unsupervised learning paradigm that partitions data into $$ disjoint clusters.<n>Recent work improved clustering quality by introducing identical frequent patterns (k-relaxed patterns)<n>This approach suffers from a critical limitation: multiple distinct k-covers may induce identical k-covers, leading to redundant symbolic representations.<n>We propose an optimization strategy that removes redundant patterns by retaining a single representative pattern for each distinct k-cover.
  arXiv  Detail & Related papers  (2026-04-14T08:45:38Z)
• Explainable cluster analysis: a bagging approach [3.5173697454104844]
  Methods rarely provide insight into which features drive the grouping of similar observations.<n>We propose an ensemble-based clustering framework that integrates bagging and feature dropout to generate feature importance scores.<n>Its effectiveness is demonstrated on multiple simulated and real-world datasets.
  arXiv  Detail & Related papers  (2026-03-20T10:34:04Z)
• Universal NP-Hardness of Clustering under General Utilities [11.62669179647184]
  We formalise the common optimisation core motivating a diverse-time computable partition utility over a finite metric space.<n>By mapping ten major paradigms -- including k-means, GMMs, DBSCAN, spectral clustering, and affinity propagation -- to the UCP framework, we demonstrate that each inherits this fundamental inability.
  arXiv  Detail & Related papers  (2026-02-27T13:08:15Z)
• Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment [15.396375506151102]
  We propose a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components.<n> Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.
  arXiv  Detail & Related papers  (2026-01-14T00:46:00Z)
• Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning [65.75756724642932]
  In incomplete multi-view clustering, missing data induce prototype shifts within views and semantic inconsistencies across views.<n>We propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL)<n>FreeCSL achieves more confident and robust assignments on IMVC task, compared to state-of-the-art competitors.
  arXiv  Detail & Related papers  (2025-05-16T12:37:10Z)
• Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective [52.662463893268225]
  Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios.<n>Existing SHGL methods encounter two significant limitations.<n>We introduce a novel framework enhanced by rank and dual consistency constraints.
  arXiv  Detail & Related papers  (2024-12-01T09:33:20Z)
• Self-Supervised Graph Embedding Clustering [70.36328717683297]
  K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
  arXiv  Detail & Related papers  (2024-09-24T08:59:51Z)
• Sanitized Clustering against Confounding Bias [38.928080236294775]
  This paper presents a new clustering framework named Sanitized Clustering Against confounding Bias (SCAB) SCAB removes the confounding factor in the semantic latent space of complex data through a non-linear dependence measure. Experiments on complex datasets demonstrate that our SCAB achieves a significant gain in clustering performance.
  arXiv  Detail & Related papers  (2023-11-02T14:10:14Z)
• Multi-View Clustering via Semi-non-negative Tensor Factorization [120.87318230985653]
  We develop a novel multi-view clustering based on semi-non-negative tensor factorization (Semi-NTF) Our model directly considers the between-view relationship and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point.
  arXiv  Detail & Related papers  (2023-03-29T14:54:19Z)
• You Never Cluster Alone [150.94921340034688]
  We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation. We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
  arXiv  Detail & Related papers  (2021-06-03T14:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.