CAPKM++2.0: An upgraded version of the collaborative annealing power k-means++ clustering algorithm

Hongzong Li, Jun Wang

Abstract: The collaborative annealing power k-means++ (CAPKM++) clustering algorithm has been recently proposed based on multiple modules by minimizing annealed power-mean functions. This paper presents an upgraded version of CAPKM++ called CAPKM++2.0. Different from CAPKM++ where the anchor points of surrogate functions for majorizing the power-mean functions are re-initialized and minimized repeatedly after annealing, CAPKM++2.0 re-initializes the weights of the majorization function during annealing. In addition, unlike CAPKM++ that minimizes the majorization function of the power-mean sum, CAPKM++2.0 adds an inner loop to minimize the power-mean sum iteratively and locally at every annealing step. Ablation study results are discussed to justify the adoption of the power-mean and the collaboration of multiple modules. Experimental results on sixteen benchmark datasets are elaborated to demonstrate the superior clustering performance of the upgraded algorithm compared with its predecessor and six other mainstream algorithms in terms of cluster validity indices and algorithmic complexities.