Open AccessA New Soft Computing Method for K-Harmonic Means Clustering
Author(s) -
Wei-Chang Yeh,
Yunzhi Jiang,
Yee-Fen Chen,
Zhe Chen
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0164754
Subject(s) - cluster analysis , initialization , benchmark (surveying) , harmonic , swarm behaviour , computer science , centroid , cluster (spacecraft) , pattern recognition (psychology) , artificial intelligence , algorithm , physics , geography , cartography , quantum mechanics , programming language
The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.