Clustering Validation via Sample Pair Co-Cluster Testing

Xinying Liu

doi:10.1109/TKDE.2025.3631331

Clustering Validation via Sample Pair Co-Cluster Testing

2025-11-10

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DOI：10.1109/TKDE.2025.3631331

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Clustering validation is a fundamental task in cluster analysis. While many clustering validity indices have been proposed, most existing internal validity indices are typically defined heuristically, lacking solid statistical foundation and interpretation. To address this limitation, we introduce a new internal validity index that employs hypothesis testing to determine whether two samples belong to the same cluster and defines the index as the proportion of correctly identified sample pairs based on their cluster memberships. To demonstrate the advantages of proposed validity index, we conduct experiments on various synthetic and real-world data sets. The experimental results indicate that our validation index can beat both classic and state-of-the-art internal validation indices.

Keyword:

Clustering analysis

clustering validity index

hypothesis testing

statistical significance

Clustering Validation via Sample Pair Co-Cluster Testing

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