Sparsity-aware Possibilistic Clustering Algorithms

TL;DR

This paper introduces two sparsity-aware possibilistic clustering algorithms that effectively handle clusters with varying densities and variances, including automatic estimation of the number of clusters.

Contribution

The paper proposes novel sparsity-aware possibilistic clustering algorithms with dynamic parameter adaptation and cluster number estimation capabilities.

Findings

Effective in clustering closely located, density-varying clusters

Improved cluster representative estimates

Ability to estimate the true number of clusters

Abstract

In this paper two novel possibilistic clustering algorithms are presented, which utilize the concept of sparsity. The first one, called sparse possibilistic c-means, exploits sparsity and can deal well with closely located clusters that may also be of significantly different densities. The second one, called sparse adaptive possibilistic c-means, is an extension of the first, where now the involved parameters are dynamically adapted. The latter can deal well with even more challenging cases, where, in addition to the above, clusters may be of significantly different variances. More specifically, it provides improved estimates of the cluster representatives, while, in addition, it has the ability to estimate the actual number of clusters, given an overestimate of it. Extensive experimental results on both synthetic and real data sets support the previous statements.