Modal clustering on PPGMMGA projection subspace

TL;DR

This paper introduces a modal clustering method for PPGMMGA projection subspaces, employing a modal EM algorithm to identify density modes and assign data points to clusters, enhancing visualization-based clustering.

Contribution

It proposes a novel modal clustering approach integrated with PPGMMGA for improved cluster detection in projected data spaces.

Findings

Effective clustering demonstrated on simulated data

Application to real data shows practical utility

Improved cluster identification over existing methods

Abstract

PPGMMGA is a Projection Pursuit (PP) algorithm aimed at detecting and visualizing clustering structures in multivariate data. The algorithm uses the negentropy as PP index obtained by fitting Gaussian Mixture Models (GMMs) for density estimation, and then optimized using Genetic Algorithms (GAs). Since the PPGMMGA algorithm is a dimension reduction technique specifically introduced for visualization purposes, cluster memberships are not explicitly provided. In this paper a modal clustering approach is proposed for estimating clusters of projected data points. In particular, a modal EM algorithm is employed to estimate the modes corresponding to the local maxima in the projection subspace of the underlying density estimated using parsimonious GMMs. Data points are then clustered according to the domain of attraction of the identified modes. Simulated and real data are discussed to illustrate the proposed method and evaluate the clustering performance.