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arXiv · 2211.03956

Significance-Based Categorical Data Clustering

Lianyu Hu·Mudi Jiang·Yan Liu·Zengyou He
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Abstract

Although numerous algorithms have been proposed to solve the categorical data clustering problem, how to access the statistical significance of a set of categorical clusters remains unaddressed. To fulfill this void, we employ the likelihood ratio test to derive a test statistic that can serve as a significance-based objective function in categorical data clustering. Consequently, a new clustering algorithm is proposed in which the significance-based objective function is optimized via a Monte Carlo search procedure. As a by-product, we can further calculate an empirical $p$-value to assess the statistical significance of a set of clusters and develop an improved gap statistic for estimating the cluster number. Extensive experimental studies suggest that our method is able to achieve comparable performance to state-of-the-art categorical data clustering algorithms. Moreover, the effectiveness of such a significance-based formulation on statistical cluster validation and cluster number estimation is demonstrated through comprehensive empirical results.

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Lianyu Hu, Mudi Jiang, Yan Liu, Zengyou He. 2022-11-08. Significance-Based Categorical Data Clustering. https://arxiv.org/abs/2211.03956

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