Work

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

Year: 2023

Type: article

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.

Source: Journal of Probability and Statistics

DOI: 10.1155/2023/8849404

Authors: Mohammed H. Baragilly, Hend Gabr, Brian H. Willis

Institutions: University of Birmingham, Helwan University, University of Manchester, Menoufia University

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,050

Funder: Medical Research Council