Abstract
Clustering is the unsupervised classification of data items of patterns into groups, each of which should be as homogeneous as possible. The problem of clustering has been addressed in many contexts in many disciplines and this reflects its broad appeal and usefulness in exploratory data analysis. This paper presents a new clustering algorithm, called GHSBEEK which is a combination of the Global best Harmony search (GHS) with features of Artificial Bee Colony (ABC) and K-means algorithms. Global-best Harmony search (GHS) is a derivative-free optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. It has a remarkable advantage of algorithm simplicity. However, it suffers from a slow search speed. The ABC algorithm is applied to improve the members of the Harmony Memory based on their fitness values and hence improves the convergence rate of the Harmony Search method. The GHSBEEK algorithm has been used for data clustering on several benchmark data sets. The clustering performance of the proposed algorithm is compared with the GHS, PSO, and K-means. The simulation results show that the proposed algorithm outperforms the other algorithms in terms of accuracy, robustness, and convergence speed.
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References
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM, 0360-0300/99/0900-0001 (2000)
Bratton, D., Kennedy, J.: Defining a Standard for Particle Swarm Optimization. In: Proc. Of the IEEE Swarm Intelligence Symposium (SIS), pp. 120–127 (2007)
Karaboga, D., Basturk, B.: Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007)
Karaboga, D.: An idea based on honey bee swarm for Numerical optimization. Technical Report TR06, Erciyes University, Engineering faculty, Computer Engineering Department (2005)
Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering 194(36-38), 3902–3922 (2005)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Liu, K., Tan, Y., He, X.: Particle Swarm Optimization Based Learning Method for Process Neural Networks. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010, Part I. LNCS, vol. 6063, pp. 280–287. Springer, Heidelberg (2010)
Omran, M.G.H., Mahdavi, M.: Global-best Harmony Search. Appl. Math. Comput. 198, 643–656 (2008)
Redmondand, S.J., Heneghan, C.: A method for initializing the K-means clustering algorithm using kd trees. Pattern Recognition Letters 28, 965–973 (2007)
Kang, S.L., Geem, Z.W.: A new structural optimization method based on the Harmony search Algorithm. Computers and Structures 82(9-10), 781–798 (2004)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: Harmony Search Optimization: application to pipe network design. International Journal of Modeling and Simulation 22(2), 125–133 (2002)
UCI Machine Learning Repository: datasets, http://archive.ics.uci.edu/ml/datasets.html
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Krishnaveni, V., Arumugam, G. (2012). The Performance Analysis of a Novel Enhanced Artificial Bee Colony Inspired Global Best Harmony Search Algorithm for Clustering. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds) Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27443-5_3
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DOI: https://doi.org/10.1007/978-3-642-27443-5_3
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