Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach

Mohammed H. Baragilly, Hend Gabr, Brian H. Willis and Hyungjun Cho

Journal of Probability and Statistics, 2023, vol. 2023, 1-15

Abstract: Determining the right number of clusters without any prior information about their numbers is a core problem in cluster analysis. In this paper, we propose a nonparametric clustering method based on different weighted spatial rank (WSR) functions. The main idea behind WSR is to define a dissimilarity measure locally based on a localized version of multivariate ranks. We consider a nonparametric Gaussian kernel weights function. We compare the performance of the method with other standard techniques and assess its misclassification rate. The method is completely data-driven, robust against distributional assumptions, and accurate for the purpose of intuitive visualization and can be used both to determine the number of clusters and assign each observation to its cluster.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljps:8849404

DOI: 10.1155/2023/8849404

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