Spontaneous Clustering via Minimum \gamma-divergence

TL;DR

This paper introduces a novel clustering method called spontaneous clustering that automatically determines the number of clusters by minimizing the b3-divergence, outperforming traditional methods that require pre-specifying cluster count.

Contribution

The paper presents a new clustering approach based on local minimization of b3-divergence, capable of automatically detecting the number of clusters without prior specification.

Findings

Automatically detects the number of clusters

Outperforms existing methods in simulations

Validated with real data analysis

Abstract

We propose a new method for clustering based on the local minimization of the \gamma-divergence, which we call the spontaneous clustering. The greatest advantage of the proposed method is that it automatically detects the number of clusters that adequately reflect the data structure. In contrast, exiting methods such as K-means, fuzzy c-means, and model based clustering need to prescribe the number of clusters. We detect all the local minimum points of the \gamma-divergence, which are defined as the centers of clusters. A necessary and sufficient condition for the \gamma-divergence to have the local minimum points is also derived in a simple setting. A simulation study and a real data analysis are performed to compare our proposal with existing methods.