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Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes

Summary

Researchers from Hong Kong Baptist University propose a novel distance metric and clustering algorithm for categorical data with both nominal and ordinal attributes. The method unifies the handling of both attribute types while preserving ordinal order information, and jointly learns distance weights and data partitions to avoid suboptimal solutions.

SourcearXiv Machine LearningAuthor: Yiqun Zhang, Yiu-ming Cheung
Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes
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[Submitted on 6 Jul 2026]

Title:Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes

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Abstract:The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects. However, most of the existing clustering methods treat the two categorical subtypes, i.e. nominal and ordinal attributes, in the same way when calculating the dissimilarity without considering the relative order information of the ordinal values. Moreover, there would exist interdependence among the nominal and ordinal attributes, which is worth exploring for indicating the dissimilarity. This paper will therefore study the intrinsic difference and connection of nominal and ordinal attribute values from a perspective akin to the graph. Accordingly, we propose a novel distance metric to measure the intra-attribute distances of nominal and ordinal attributes in a unified way, meanwhile preserving the order relationship among ordinal values. Subsequently, we propose a new clustering algorithm to make the learning of intra-attribute distance weights and partitions of data objects into a single learning paradigm rather than two separate steps, whereby circumventing a suboptimal solution. Experiments show the efficacy of the proposed algorithm in comparison with the existing counterparts.

Comments: 16 pages, 11 figures

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.05464 [cs.LG]

(or arXiv:2607.05464v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.05464

arXiv-issued DOI via DataCite (pending registration)

Journal reference: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

Related DOI:

https://doi.org/10.1109/TPAMI.2021.3056510

DOI(s) linking to related resources

Submission history

From: Yiqun Zhang [view email] [v1] Mon, 6 Jul 2026 04:38:39 UTC (279 KB)

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Key points and analysis

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Key points

  • Existing methods treat nominal and ordinal attributes identically, ignoring order information.
  • A graph-inspired unified distance metric preserves ordinal relationships.
  • The new clustering algorithm jointly learns distance weights and partitions in a single paradigm.
  • Experiments on multiple real datasets demonstrate superior performance.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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