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Evolutionary Improved Swarm-Based Hybrid K-Means Algorithm for Cluster Analysis

  • Janmenjoy Nayak
  • D. P. Kanungo
  • Bighnaraj Naik
  • H. S. Behera
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)

Abstract

Improvement in the quality of cluster centers and minimization of intra-cluster distance are two most challenging areas of K-means clustering algorithm. Due to predetermined number of clusters, it is difficult to predict the exact value of k. Furthermore, in case of non-globular clusters, K-means fails to get optimal cluster center in a data set. In this paper, a hybrid improved particle swarm optimization-based evolutionary K-means clustering method has been proposed to obtain the optimal cluster center. The hybridization of improved PSO and genetic algorithm (GA) along with K-means algorithm improves the convergence speed as well as helps to find the global optimal solution. In the first stage, IPSO has been used to get a global solution in order to get optimal cluster centers. Then, the crossover steps of GA are used to improve the quality of particles and mutation is used for diversification of solution space in order to avoid premature convergence. The performance analysis of the proposed method is compared with other existing clustering techniques like K-means, GA-K-means, and PSO-K-means.

Keywords

Clustering K-means Improved PSO GA Hybrid GA-IPSO 

Notes

Acknowledgments

This work is supported by the Department of Science & Technology (DST), Ministry of Science & Technology, New Delhi, Govt. of India, under grants No. DST/INSPIRE Fellowship/2013/585.

References

  1. 1.
    Hartigan, J.A.: Clustering algorithms. 1975, Wiley, New YorkGoogle Scholar
  2. 2.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A K-means clustering algorithm. J. Royal Stat. Soc. C 28(1), 100–108. JSTOR 2346830 (1979)Google Scholar
  3. 3.
    Ensafi, R., Dehghanzadeh, S., Akbarzadeh, T.M.R.: Optimizing fuzzy K-means for network anomaly detection using PSO, pp. 686–693 (2008). doi:  10.1109/AICCSA.2008.4493603
  4. 4.
    Sha, M., Yang, H.: Speaker recognition based on APSO-K-means clustering algorithm. In: International Conference on Artificial Intelligence and Computational Intelligence, (2009). doi:  10.1109/AICI.2009.17
  5. 5.
    Kader, A.R.F.: Genetically improved PSO algorithm for efficient data clustering. In: Second International Conference on Machine Learning and Computing, 2010. doi: 10.1109/ICMLC.2010.19 (2010)
  6. 6.
    Xiangwei, Z., Yuanjiang, J.: A study on educational data clustering approach based on improved particle swarm optimizer (2011). doi: 10.1109/ITiME.2011.6132144 Google Scholar
  7. 7.
    Youguo, L., Haiyan, W.: A clustering method based on K-means algorithm. Phys. Procedia 25, 1104–1109 (2012)CrossRefGoogle Scholar
  8. 8.
    Naik, B., Swetanisha, S., Behera, D. K., Mahapatra, S., Padhi, B.K.: Cooperative swarm based clustering algorithm based on PSO and k-means to find optimal cluster centroids. In: National Conference on Computing and Communication Systems (NCCCS), pp. 1–5 (2012). doi: 10.1109/NCCCS.2012.6413027 (2012)
  9. 9.
    Govindarajan, K., Somasundaram, T.S. Vivekanandan, K.S.K.: Particle swarm optimization (PSO)-based clustering for improving the quality of learning using cloud computing. In: IEEE 13th International Conference on Advanced Learning Technologies, pp. 495–497 (2013). doi: 10.1109/ICALT.2013.160 (2013)
  10. 10.
    Liao, Q., Yang, F., Zhao, J.: An Improved parallel K-means clustering algorithm with map reduce, In: Proceedings of ICCT, pp. 764–768, (2013). doi: 10.1109/ICCT.2013.6820477
  11. 11.
    Bai, L., Liang, J., Chao, S., Dang, C.: Fast global k-means clustering based on local geometrical information. Inf. Sci. 245, 168–180 (2013)CrossRefGoogle Scholar
  12. 12.
    Liao, K., Liu, G., Xiao, L., Chaoteng L.: A sample-based hierarchical adaptive K-means clustering method for large-scale video retrieval. Knowl.-Based Syst. 49, 123–133 (2013)Google Scholar
  13. 13.
    Jaganathan, P., Jaiganesh, S.: An improved K-means algorithm combined with Particle Swarm Optimization approach for efficient web document clustering, pp. 772–776, (2013). doi: 10.1109/ICGCE.2013.6823538
  14. 14.
    Yao, H., Duan, Q., Li, D., Wang, L.: An improved K-means clustering algorithm for fish image segmentation. Math. Comput. Model. 58, 790–798 (2013)MATHCrossRefGoogle Scholar
  15. 15.
    Monedero, D.R., Solé, M., Nin, J., Forné, J.: A modification of the k-means method for quasi-unsupervised learning. Knowl.-Based Syst. 37, 176–185 (2013)CrossRefGoogle Scholar
  16. 16.
    Shahbaba, M., Beheshti, S.: MACE-means clustering. Sig. Process. 105, 216–225 (2014)CrossRefGoogle Scholar
  17. 17.
    Naldi, M.C., Campello, R.J.G.B.: Evolutionary k-means for distributed datasets. Neurocomputing 127, 30–42 (2014)CrossRefGoogle Scholar
  18. 18.
    Scitovski, R., Sabo, K.: Analysis of the k-means algorithm in the case of data points occurring on the border of two or more clusters, Knowl.-Based Syst. 57, 1–7 (2014)Google Scholar
  19. 19.
    Pavithra, M.S.: Aradhya Manjunath VN : A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video. Appl. Comput. Inf. (2014). doi: 10.1016/j.aci.2014.08.001 Google Scholar
  20. 20.
    Kennedy, J., Eberhart, R.: Particle swarm optimization, In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4,1942–1948 (1995)Google Scholar
  21. 21.
    Wei, J., Guangbin, L., Dong, L.: Elite particle swarm optimization with mutation. In: IEEE Asia Simulation Conference—7th International Conference on System Simulation and Scientific Computing, pp. 800–803 (2008)Google Scholar
  22. 22.
    Khare, A., Rangnekar, S.: A review of particle swarm optimization and its applications in solar photovoltaic system. Appl. Soft Comput. 13, 2997–3006 (2013)CrossRefGoogle Scholar
  23. 23.
    Babaei, M.: A general approach to approximate solutions of nonlinear differential equations using particle swarm optimization. Appl. Soft Comput. 13, 3354–3365 (2013)CrossRefGoogle Scholar
  24. 24.
    Neri, F., Mininno, E., Iacca, G.: Compact particle swarm optimization. Inf. Sci. 239, 96–121 (2013)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Nayak, J., Naik, B., Kanungo, D.P., Behera, H.S.: An improved swarm based hybrid k-means clustering for optimal cluster centers. In: Information Systems Design and Intelligent Applications, pp. 545–553. Springer, (2015)Google Scholar
  26. 26.
    Yue-bo, M., Jian-hua, Z., Xu-sheng, G., Liang, Z.: Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm. Neurocomputing 83, 212–221 (2012)Google Scholar
  27. 27.
    Dehuri, S., Roy, R., Cho, S.B., Ghosh, A.: An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J. Syst. Softw. 85, 1333–1345 (2012)CrossRefGoogle Scholar
  28. 28.
    Kanungo, D.P., Naik, B., Nayak, J., Baboo, S., Behera, H.S.: An improved PSO based back propagation learning—MLP(IPSO-BP-MLP) for classification, Smart Innovation, Systems and Technologies 31, doi: 10.1007/978-81-322-2205-7_32 (2015)
  29. 29.
    Bache, K., Lichman, M.: UCI machine learning repository [http://archive.ics.uci.edu/ml], Irvine, University of California, School of Information and Computer Science (2013)

Copyright information

© Springer India 2016

Authors and Affiliations

  • Janmenjoy Nayak
    • 1
  • D. P. Kanungo
    • 1
  • Bighnaraj Naik
    • 1
  • H. S. Behera
    • 1
  1. 1.Department of CSE and ITVeer Surendra Sai University of TechnologyOdishaIndia

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