A novel harmony search-based approach for clustering problems Online publication date: Thu, 30-Jun-2016
by J. Senthilnath; Sushant Kulkarni; D.R. Raghuram; Meghana Sudhindra; S.N. Omkar; Vipul Das; V. Mani
International Journal of Swarm Intelligence (IJSI), Vol. 2, No. 1, 2016
Abstract: Harmony search (HS) is a music-based metaheuristic optimisation algorithm. The harmony in music is analogous to finding the optimality in an optimisation process. In this paper, HS is used to obtain the cluster centres and then classify the data. Three typical benchmark datasets from the UCI machine learning repository and a real time multispectral satellite image are used to demonstrate the results of the technique. The performance of HS is compared with a conventional clustering technique - K-means, and three metaheuristic methods, namely, genetic algorithm (GA), particle swarm optimisation (PSO) and cuckoo search (CS). The results are evaluated using four performance measures, namely, classification error percentage, receiver operating characteristics, time complexity and statistical significance test. From the obtained results, we conclude that HS can be efficiently used to solve data clustering problems.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Swarm Intelligence (IJSI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook