K-Means Clustering Algorithm Method Based on Shuffled Frog Leaping Algorithm

Abstract:

Aiming to resolve the problems of the traditional k-means clustering algorithm such as random selecting of initial clustering centers,the low efficiency of clustering,low in the real,this paper proposed a novel k-means clustering algorithm method based on shuffled frog leaping algorithm.This algorithm combined the advantages of k-means algorithm and shunffled forg leaping algorithm.A chaotic local search was introduced to improve the quality of the initial individual,a new searching strategy was presented to update frog position,that increased the optimization ability of algorithm.According to the variation of the frog’s finess variance used k-means algorithm,it has the advantages in the global search ability and convergence speed.The experimental results show that this algorithm has higher accuracy..

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2245-2249
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2245
• Online since: July 2014
• Authors: Zhe Tang*, Ke Luo
• Keywords: Ant Colony Optimization Algorithm, Cluster, Genetic Algorithm (GA), K-Mean, Shuffled Frog-Leaping Algorithm

References

[1] Chen Guangping, Wang Wenpeng, Huan Jun. Improved Initial Clustering Center Selection Meth-od for K-means Algorithm,J. Journal of Chinese Computer Systems 2012, 33(6): 1320-1323.

[2] Wang Bingjing, Gao Maoting. New model for clustering ensemble based on genetic algorithms,J. Computer Engin-eering and Applications, 2013, 49(8): 164-168.

[3] Zhang Chunkai, Wang Lijun. Improved K-means clustering algorithm based on genetic algorit-hm,J. Computer Engineering and Applications, 2012, 48(26): 144-147.

[4] Li Yafei, Cao Changhu. Collabrative Clustering Algorithm Based on Particle Swarm Optimizati-on and Genetic Algorithm,J. Computer Engineering, 2011, 37(16): 167-169.

[5] Meng Ying, Luo Ke, et al. K-medoids clustering algorith method based on ant colony algorithm,J. Computer Engineering and Applications , 2012, 48(16): 136-139.

[6] Eusuff M M, Lansey K E. Optimization of wat-er distribution network design using the shuffle-d frog leaping algorithm,J. Journal of Water Sources Planning and Management, 2003, 129(3): 210-225.

DOI: 10.1061/(asce)0733-9496(2003)129:3(210)

[7] Cui W, Liu X, et al. Survey on shuffled frog leaping algorithm,J. Control and Decision, 2012, 27(4): 481-486.

[8] Wang L, Dai Y. A Multiagent Shuffled Frog Leaping Algorithm,J. Computer Engineering, 2013, 39(7): 265-269.

[9] Matus K, Geubelle P H. Multiscale modeling of particle debonding in reinforced elastomers sub-jected to finite deformations,J. International Journal for Numerical Methods in Engineering, 2006, 65(2): 190-223.

DOI: 10.1002/nme.1446

[10] Nie Rui, Zhang Weiguo, et al. Adaptive chaos hybrid multi-objective genetic algorithm based o-n the Tent map,J. Journal of Beijing University of Aeronautics and Astronautics. 2012, 38(8): 1010-1016.

[11] Xie Shao-bin, Wang An-an, et al. Anti-multipath Performance of Improved Tent Chaos-based FM Radar signal,J. Modern Radar, 2012, 34(1): 25-29.