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Arxiv
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Two-cluster test
- Cluster analysis is a key topic in statistics and machine learning, with the challenge of determining if two sample subsets belong to the same cluster.
- Classic two-sample tests used in clustering scenarios can lead to inflated Type-I error rates, necessitating the development of a new approach known as the two-cluster test.
- A novel method utilizing boundary points between subsets is introduced to calculate analytical p-values, effectively reducing the Type-I error rate compared to traditional two-sample tests.
- Experiments on synthetic and real datasets demonstrate the effectiveness of the proposed two-cluster test in various clustering applications, including tree-based interpretable clustering and significance-based hierarchical clustering.
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