title: A New Soft Computing Method for K-Harmonic Means Clustering.
abstract: 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.
authors.affiliation: Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu City, Taiwan.
authors.forename: Wei-Chang
authors.identifier: http://orcid.org/0000-0001-7393-0768
authors.initials: WC
authors.lastname: Yeh
authors.affiliation: School of Automation, Guangdong Polytechnic Normal University, Guangdong, China.
authors.forename: Yunzhi
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: Department of Industrial Engineering and Management, Hsiuping University of Science and Technology, Taichung City, Taiwan.
authors.forename: Yee-Fen
authors.initials: YF
authors.lastname: Chen
authors.affiliation: School of Information Engineeering, Chang'an University, Xi'an, China.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D003198:Computer Simulation; D019992:Databases as Topic; D006801:Humans