Abstract
An Improved Ant Clustering Algorithm based on habitation-searching is proposed to solve the clustering problem in data mining. In this algorithm, each ant stands for one data object, and the ants search suitable places to stay according to the probability function for ants becoming active and the clustering rules, which are given in the paper. The ants affect each other in the process, in this way the clustering will be formed by dynamic self-organization for the ants. Besides, in order to improve the quality and speed of the clustering, the activation threshold changes adaptively as the algorithm runs. The achieved results are compared with those obtained by LF algorithm, showing that significant improvements are obtained by the proposed method, and demonstrating the effectiveness of the algorithm.
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References
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperative learning approach to the traveling agents. IEEE Trans Syst Man Cybernet 26(1):29–41
Gutjahr WJ (2002) ACO algorithms with guaranteed convergence to the optimal solution [J]. Inform Process Lett 82(3):145–153
Hongjian C, Ling C, Ling Q et al (2003) Application of genetic algorithms based on the strategy of gene reconfiguration [C]. The processing of the second asian workshop on foundations of Software. Southeast University Press, pp 89–92
Watanabe I, Matsui S (2003) Improving the performance of ACO algorithms by adaptive control of candidate set. Proc 2003 Congress Evol Comput 2:1355–1362
Liu LG, Feng GZ (2007) Simulated annealing based multi-constrained QoS routing in mobile ad hoc networks. Wirel Pers Commun 41:393–405
Liu S, Mao L, Yu J (2006) Path planning based on ant colony algorithm and distributed local navigation for multi-robot systems. In: Proceedings of 2006 IEEE international conference on mechatronics and automation pp 1733–1738
Deneubourg JL, Goss S, Franks N et al (1991) The dynamics of collective sorting: robot- like ant and ant- like robot [C]. In: Proceedings of the first conference on simulations of adaptive behavior: from animals to animats. MIT Press, Cambridge, pp 356–365
Lumer E, Faieta B (1994) Diversity and adaptation in populations of clustering ants [C]. In: Proceedings of the third international conference on simulation of adaptive behavior: from animals to animats. MIT Press, Cambridge, pp 499–508
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Duan, Yb., Dai, Z., Chen, Q., Shao, Ky., Xu, Ss. (2012). An Improved Ant Clustering Algorithm Based on Habitation-Searching. In: Hou, Z. (eds) Measuring Technology and Mechatronics Automation in Electrical Engineering. Lecture Notes in Electrical Engineering, vol 135. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2185-6_11
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DOI: https://doi.org/10.1007/978-1-4614-2185-6_11
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