Collaborative annealing power k-means plus plus clustering

Clustering is the most fundamental technique for data processing. This paper presents a collaborative annealing power k-means++ clustering algorithm by integrating the k-means++ and power k-means algorithms in a collaborative neurodynamic optimization framework. The proposed algorithm starts with k-means++ to select initial cluster centers, then leverages the power k-means to find multiple sets of centers as alternatives and a particle swarm optimization rule to reinitialize the centers in the subsequential iterations for improving clustering performance. Experimental results on twelve benchmark datasets are elaborated to demonstrate the superior performance of the proposed algorithm to seven mainstream clustering algorithms in terms of 21 internal and external indices. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a collaborative annealing power k-means++ clustering algorithm, which integrates the k-means++ and power k-means algorithms in a collaborative neurodynamic optimization framework to select initial cluster centers and improve clustering performance.