首页|Collaborative annealing power k-means++ clustering

Collaborative annealing power k-means++ clustering

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  • NETL
  • NSTL
  • Elsevier
© 2022 Elsevier B.V.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.

Collaborative neurodynamic optimizationk-means clusteringk-means++Power k-means

Li H.、Wang J.

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Department of Computer Science City University of Hong Kong

Department of Computer Science City University of Hong KongDepartment of Computer Science City University of Hong Kong||School of Data Science City University of Hong Kong||

2022

10.1016/j.knosys.2022.109593

Knowledge-based systems

Knowledge-based systems

EISCI
ISSN：0950-7051
年,卷(期)：2022.255(Nov.14)
  • 10
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