Collaborative annealing power k-means++ clustering

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Original languageEnglish
Article number109593
Journal / PublicationKnowledge-Based Systems
Online published24 Aug 2022
Publication statusPublished - 14 Nov 2022


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.

Research Area(s)

  • Collaborative neurodynamic optimization, k-means clustering, k-means++, Power k-means