Active Function Cross-Entropy Clustering

TL;DR

This paper introduces an active function clustering model that extends Gaussian Mixture Models to better handle nonlinear data, automatically determining the number of clusters and adapting to complex datasets.

Contribution

It proposes a new active function clustering approach that is dimension-agnostic, adaptable to various functions, and automatically reduces cluster count during the process.

Findings

Handles nonlinear and curved data effectively

Automatically determines the optimal number of clusters

Applicable to high-dimensional datasets

Abstract

Gaussian Mixture Models (GMM) have found many applications in density estimation and data clustering. However, the model does not adapt well to curved and strongly nonlinear data. Recently there appeared an improvement called AcaGMM (Active curve axis Gaussian Mixture Model), which fits Gaussians along curves using an EM-like (Expectation Maximization) approach. Using the ideas standing behind AcaGMM, we build an alternative active function model of clustering, which has some advantages over AcaGMM. In particular it is naturally defined in arbitrary dimensions and enables an easy adaptation to clustering of complicated datasets along the predefined family of functions. Moreover, it does not need external methods to determine the number of clusters as it automatically reduces the number of groups on-line.