Clustering using modified harmony search algorithm Online publication date: Wed, 11-Jun-2014
by Vijay Kumar; Jitender Kumar Chhabra; Dinesh Kumar
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 3, No. 2/3, 2014
Abstract: Metaheuristic techniques are being successfully used as optimisation methods in various application areas. This paper presents modification in one such metaheuristic called as harmony search (HS) that is inspired from music improvisation process. The two parameters, harmony memory consideration rate (HMCR) and pitch adjusting rate (PAR), in HS play important role in improvisation of new harmony. Instead of keeping the parameters fixed, as reported in many existing algorithms, these are being allowed to change dynamically during the process of improvisation in the proposed algorithm. This paper further examines the effect on the results when K-means is initialised with solution returned by the proposed algorithm. The effect of harmony memory size has also been investigated on proposed approach. The experiments are performed for data clustering on nine benchmark datasets. The clustering performance of proposed algorithm is compared with K-means, fuzzy C-means, genetic algorithm, and four recently proposed variants of HS. The results are encouraging and demonstrate that the proposed algorithm provides much better values in terms of precision, recall, G-measure, inter-cluster and intra-cluster distances.
Online publication date: Wed, 11-Jun-2014
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