### Fast Artificial Bee Colony for Clustering

#### Abstract

Artificial Bee Colony (ABC) is one of good heuristic intelligent algorithm to solve optimization problem including clustering. Generally, the heuristic algorithm will take the high computation time to solve optimization problem. Likewise, ABC also consumes the much time to solve clustering problem. This paper intends solving clustering problem using ABC with focusing reduction computation time called FABCC. This idea proposes detecting the pattern of redundant process then compacting it to effective process to diminish the computation process. There are five data sets to be used to prove the performance of FABCC. The results shows that FABCC is effective to prune the duration process up to 46.58 %.

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a. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Comput. Surv., vol. 31, no. 3, pp. 264–323, 1999.

Y. Yang and M. Kamel, “Clustering ensemble using swarm intelligence,” Swarm Intell. Symp. 2003. SIS ’03. Proc. 2003 IEEE, pp. 65–71, 2003.

T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” Proc. 1996 ACM SIGMOD Int. Conf. Manag. Data, vol. 1, pp. 103–114, 1996.

B. Akay and D. Karaboga, “A modified Artificial Bee Colony algorithm for real-parameter optimization,” Inf. Sci. (Ny)., vol. 192, pp. 120–142, 2012.

A. Ouaarab, B. Ahiod, and X.-S. Yang, “Discrete cuckoo search algorithm for the travelling salesman problem,” Neural Comput. Appl., vol. 24, no. 7–8, pp. 1659–1669, 2013.

S.-M. Chen and C.-Y. Chien, “Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques,” Expert Syst. Appl., vol. 38, no. 12, pp. 14439–14450, 2011.

C. Zhang, D. Ouyang, and J. Ning, “An artificial bee colony approach for clustering,” Expert Syst. Appl., vol. 37, no. 7, pp. 4761–4767, 2010.

D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial Bee Colony (ABC) algorithm,” Appl. Soft Comput., vol. 11, no. 1, pp. 652–657, 2011.

S. Goel, A. Sharma, and P. Bedi, “Cuckoo Search Clustering Algorithm: A novel strategy of biomimicry,” 2011 World Congr. Inf. Commun. Technol., pp. 916–921, 2011.

S. Rana, S. Jasola, and R. Kumar, “A review on particle swarm optimization algorithms and their applications to data clustering,” Artif. Intell. Rev., vol. 35, no. 3, pp. 211–222, 2011.

C.-L. Huang, W.-C. Huang, H.-Y. Chang, Y.-C. Yeh, and C.-Y. Tsai, “Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering,” Appl. Soft Comput., vol. 13, no. 9, pp. 3864–3872, 2013.

R. Storn and K. Price, “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” J. Glob. Optim., pp. 341–359, 1997.

A. S. Girsang, C.-W. Tsai, and C.-S. Yang, “A Fast Bee Colony Optimization for Traveling Salesman Problem,” 2012 Third Int. Conf. Innov. Bio-Inspired Comput. Appl., vol. 1, no. c, pp. 7–12, 2012.

Y. Lu, S. Lu, F. Fotouhi, Y. Deng, and S. Brown, “FGKA: A fast genetic k-means clustering algorithm,” Proc. 2004 ACM …, pp. 1–2, 2004.

K. D. David E. Goldberg, “A comparative analysis of selection schemes used in genetic algorithms.”

J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Ann. Phys. (N. Y)., vol. 54, p. 770, 2006.

T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and a. Y. Wu, “An efficient k-means clustering algorithm: analysis and implementation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 881–892, 2002.

J. a Hartigan and M. a Wong, “Algorithm AS 136: A k-means clustering algorithm,” Appl. Stat., vol. 28, no. 1, pp. 100–108, 1979.

D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” J. Glob. Optim., vol. 39, no. 3, pp. 459–471, 2007.

D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, “A comprehensive survey: artificial bee colony (ABC) algorithm and applications,” Artif. Intell. Rev., vol. 42, no. 1, pp. 21–57, 2014.

D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Appl. Soft Comput., vol. 8, no. 1, pp. 687–697, 2008.

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