Weighted K-Means Clustering Analysis Based on Improved Genetic Algorithm

Abstract:

An algorithm of weighted k-means clustering is improved in this paper, which is based on improved genetic algorithm. The importance of different contributors in the process of manufacture is not the same when clustering, so the weight values of the parameters are considered. Retaining the best individuals and roulette are combined to decide which individuals are chose to crossover or mutation. Dynamic mutation operators are used here to decrease the speed of convergence. Two groups of data are used to make comparisons among the three algorithms, which suggest that the algorithm has overcome the problems of local optimum and low speed of convergence. The results show that it has a better clustering.

• Periodical: Applied Mechanics and Materials (Volumes 511-512)
• Pages: 904-908
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.511-512.904
• Online since: February 2014
• Authors: Tong Jie Zhang*, Yan Cao, Xiang Wei Mu
• Keywords: Clustering, Dynamic Mutation Operator, Genetic Algorithm (GA), K-Means Algorithm

References

[1] Patel R, Raghuwanshi M M, Jaiswal A N. Modifying genetic algorithm with species and sexual selection by using K-means algorithm[C]/2009 IEEE International Advance Computing Conference, 2009：114-119.

DOI: 10.1109/iadcc.2009.4808991

[2] Hong He, Yonghong Tan. A Two-stage Genetic Algorithm for Automatic Clustering [J]. Neurocomputing, 2012, 81: 49 － 59.

DOI: 10.1016/j.neucom.2011.11.001

[3] Dong-Xia Chang, Xian-Da Zhang, Chang-Wen Zheng． Agenetic Algorithm with Gene Rearrangement for K-means Clustering［J］． Pattern Recognition, 2009, 42: 1210 － 1222.

DOI: 10.1016/j.patcog.2008.11.006

[4] Jain Anil K． Data Clustering: 50 Years Beyond k-means［J］. Pattern Recognition Letters, 2010, 31( 3) : 651 － 666.

DOI: 10.1016/j.patrec.2009.09.011

[5] Swagatam Das, Archana Chowdhury, Ajith Abraham. A bacterial evo lectionary algorithm for automatic data clustering [ A] . Evolutionary Computation, CECc 09 IEEE Congress on [ C ] . IEEE, 2009. 2403- 2410.

DOI: 10.1109/cec.2009.4983241

[6] Fuyuan Cao , Jiye Liang , Liang Bai. A new initialization method for categorical data clustering [ J] . Expert Systems with Applications, 2009, 36: 10223- 10228.

DOI: 10.1016/j.eswa.2009.01.060

[7] A K Jain. Data clustering : 50 years beyond K-means[ J] . Pattern Recognition Letters, 2010, 31( 8) : 651- 666.

DOI: 10.1016/j.patrec.2009.09.011

[8] Hall L O, Ozyurt I B, Bezdek J C. Clustering with a genetically optimized approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(2): 103-112.

DOI: 10.1109/4235.771164

[9] Lei Zhang, Renhou Li. Artifical C-means Clustering Algorithm[J]. Xi'an Transportation University journal. 2005, 39(8): 836-839.