The K-harmonic means clustering algorithm (KHM) is a new clustering method used to\ngroup data such that the sum of the harmonic averages of the distances between each entity\nand all cluster centroids is minimized. Because it is less sensitive to initialization than Kmeans\n(KM), many researchers have recently been attracted to studying KHM. In this study,\nthe proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO)\nand integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of\nthe utility of the proposed iSSO-KHM, we present extensive computational results on eight\nbenchmark problems. From the computational results, the comparison appears to support\nthe superiority of the proposed iSSO-KHM over previously developed algorithms for all\nexperiments in the literature.
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