Density-Adaptive Clustering of Multivariate Angular Data Using Dirichlet Process Mixture Models with Circular Normal Distribution for Artificial Intelligence Applications

Abstract

Data clustering is an essential technique for organizing unsupervised data, extracting subjects automatically, and swiftly retrieving or filtering information. In this study, we approach the task of clustering multivariate angular distributions using nonparametric Bayesian mixture models featuring von Mises distributions. Our approach operates within a nonparametric Bayesian framework, specifically leveraging the Dirichlet process. Unlike finite mixture models, our approach assumes an infinite number of clusters initially, inferring the optimal number automatically from the data. Morever, our paper introduces a unified approach, leveraging Ward's algorithm, Dirichlet process, and von Mises Mixture distributions (DPM-MvM), to effectively capture both the structure and variability inherent in the data. We've developed a variational inference algorithm for DPM-MvM enabling automatic determination of the number of clusters. Our experimental results showcase the efficiency and accuracy of our method for analyzing multivariate angular data with state of the art approaches.