Data Clustering Using Harmony Search Algorithm
Being one of the main challenges to clustering algorithms, the sensitivity of fuzzy c-means (FCM) and hard c-means (HCM) to tune the initial clusters centers has captured the attention of the clustering communities for quite a long time. In this study, the new evolutionary algorithm, Harmony Search (HS), is proposed as a new method aimed at addressing this problem. The proposed approach consists of two stages. In the first stage, the HS explores the search space of the given dataset to find out the near-optimal cluster centers. The cluster centers found by the HS are then evaluated using reformulated c-means objective function. In the second stage, the best cluster centers found are used as the initial cluster centers for the c-means algorithms. Our experiments show that an HS can minimize the difficulty of choosing an initialization for the c-means clustering algorithms. For purposes of evaluation, standard benchmark data are experimented with, including the Iris, BUPA liver disorders, Glass, Diabetes, etc. along with two generated data that have several local extrema.
KeywordsCluster Center Harmony Search Harmony Search Algorithm Harmony Memory Artificial Dataset
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- 8.Lili, L., Xiyu, L., Mingming, X.: A novel fuzzy clustering based on particle swarm optimization. In: First IEEE International Symposium on Information Technologies and Applications in Education, ISITAE, pp. 88–90 (2007)Google Scholar
- 16.Wang, X., Gao, X.Z., Ovaska, S.J.: A hybrid optimization method for fuzzy classification systems. In: Eighth International Conference on Hybrid Intelligent Systems, HIS 2008, pp. 264–271 (2008)Google Scholar
- 17.Malaki, M., Pourbaghery, J.A., Abolhassani, H.: A combinatory approach to fuzzy clustering with harmony search and its applications to space shuttle data. In: SCIS & ISIS 2008, Nagoya, Japan (2008)Google Scholar
- 18.Alia, O.M., Mandava, R., Aziz, M.E.: A hybrid harmony search algorithm to MRI brain segmentation. In: The 9th IEEE International Conference on Cognitive Informatics, ICCI 2010, pp. 712–719. IEEE, Tsinghua University (2010)Google Scholar
- 19.Forgy, E.: Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications. Biometrics 21(3), 768 (1965)Google Scholar
- 20.Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers (1981)Google Scholar
- 22.Alata, M., Molhim, M., Ramini, A.: Optimizing of fuzzy c-means clustering algorithm using GA. Proceedings of World Academy of Science, Engineering and Technology 29 (2008)Google Scholar
- 23.Al-Betar, M., Khader, A.: A hybrid harmony search for university course timetabling. In: Proceedings of the 4nd Multidisciplinary Conference on scheduling: Theory and Applications (MISTA 2009), Dublin, Ireland, pp. 10–12 (August 2009)Google Scholar
- 24.Al-Betar, M., Khader, A.: A harmony search algorithm for university course timetabling. Annals of Operations Research, 1–29 (2008)Google Scholar
- 25.Al-Betar, M., Khader, A., Nadi, F.: Selection mechanisms in memory consideration for examination timetabling with harmony search. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1203–1210. ACM (2010)Google Scholar