[2603.04458] Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering

Computer Science > Machine Learning arXiv:2603.04458 (cs) [Submitted on 3 Mar 2026] Title:Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering Authors:Yiqun Zhang, Mingjie Zhao, Yizhou Chen, Yang Lu, Yiu-ming Cheung View a PDF of the paper titled Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering, by Yiqun Zhang and 4 other authors View PDF HTML (experimental) Abstract:Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicate tendencies between two concepts (e.g., high and low temperature) with their values in well-defined Euclidean distance space, categorical attribute values are different concepts (e.g., different occupations) embedded in an implicit space. Simultaneously exploiting these two very different types of information is an unavoidable but challenging problem, and most advanced attempts either encode the heterogeneous numerical and categorical attributes into one type, or define a unified metric for them for mixed data clustering, leaving their inherent connection unrevealed. This paper, therefore, studies the connection among any-type of attributes and proposes a novel Heterogeneous Attribute Reconstruction and Representation (HARR) learning paradigm accordingly for cluster analysis. The paradigm transforms heterogeneous attributes into a homogeneous status for distance metric learning, an...