Estimation of the number of clusters on d-dimensional sphere

TL;DR

This paper introduces SX-means, a model-based method for estimating the number of clusters in spherical data using von Mises-Fisher distributions, applicable across various scientific fields.

Contribution

The paper proposes a novel SX-means algorithm specifically designed for spherical data, addressing the gap in existing clustering methods for such data types.

Findings

SX-means accurately estimates the number of clusters in spherical data.

The method performs well across different dimensions and data distributions.

Experimental results demonstrate its effectiveness compared to existing approaches.

Abstract

Spherical data is distributed on the sphere. The data appears in various fields such as meteorology, biology, and natural language processing. However, a method for analysis of spherical data does not develop enough yet. One of the important issues is an estimation of the number of clusters in spherical data. To address the issue, I propose a new method called the Spherical X-means (SX-means) that can estimate the number of clusters on d-dimensional sphere. The SX-means is the model-based method assuming that the data is generated from a mixture of von Mises-Fisher distributions. The present paper explains the proposed method and shows its performance of estimation of the number of clusters.