Abstract
Clustering is one of most commonly used approach in the literature of Pattern recognition and Machine Learning. K-means clustering algorithm is a fast and simple method in the clustering approaches. However, due to random selection of center of clusters and the adherence to preliminary results of center of clusters, the risk of trapping to a local minimum ever exist.in this study, we have taken help of effective hybrid of optimization algorithms, artificial bee colony (ABC) and differential evolution (DE), is proposed as a method to mentioned problems. The proposed method consists of two main steps. In first step, Seed Cluster Center Algorithm employed to best initial cluster centers. The combined evolutionary algorithm explores the solution space to find global solution. The performance of proposed method evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in literature confirms its superior performance and higher efficiency.
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References
Gan, G., Ma, C., Wu, J.: Data clustering: theory, algorithms, and applications, vol. 20. SIAM (2007)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)
Niknam, T., Amiri, B.: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing 10(1), 183–197 (2010)
Nguyen, C.D., Cios, K.J.: GAKREM: A novel hybrid clustering algorithm. Information Sciences 178(22), 4205–4227 (2008)
Kao, Y.-T., Zahara, E., Kao, I.W.: A hybridized approach to data clustering. Expert Systems with Applications 34(3), 1754–1762 (2008)
Khan, S.S., Ahmad, A.: Cluster center initialization algorithm for K-means clustering. Pattern Recognition Letters 25(11), 1293–1302 (2004)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)
Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Systems with Applications 37(7), 4761–4767 (2010)
Rocca, P., Oliveri, G., Massa, A.: Differential Evolution as Applied to Electromagnetics. IEEE Antennas and Propagation Magazine 53(1), 38–49 (2011)
Abbasgholipour, M., et al.: Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions. Expert Systems with Applications 38(4), 3671–3678 (2011)
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Bonab, M.B., Hashim, S.Z.M., Alsaedi, A.K.Z., Hashim, U.R. (2015). Modified K-means Combined with Artificial Bee Colony Algorithm and Differential Evolution for Color Image Segmentation. In: Phon-Amnuaisuk, S., Au, T. (eds) Computational Intelligence in Information Systems. Advances in Intelligent Systems and Computing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-319-13153-5_22
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DOI: https://doi.org/10.1007/978-3-319-13153-5_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13152-8
Online ISBN: 978-3-319-13153-5
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