High-dimensional clustering via Random Projections

TL;DR

This paper introduces a high-dimensional clustering method using random projections and ensemble techniques, improving clustering performance by selecting the best projections and aggregating results.

Contribution

It proposes a novel ensemble clustering approach based on random projections and consensus, enhancing clustering accuracy in high-dimensional spaces.

Findings

Effective on real and simulated datasets

Outperforms traditional clustering methods in high dimensions

Shows promise as a versatile high-dimensional clustering tool

Abstract

In this work, we address the unsupervised classification issue by exploiting the general idea of Random Projection Ensemble. Specifically, we propose to generate a set of low dimensional independent random projections and to perform model-based clustering on each of them. The top $B^*$ projections, i.e. the projections which show the best grouping structure are then retained. The final partition is obtained by aggregating the clusters found in the projections via consensus. The performances of the method are assessed on both real and simulated datasets. The obtained results suggest that the proposal represents a promising tool for high-dimensional clustering.