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Comparative Study of PSO-Based Hybrid Clustering Algorithms for Wireless Sensor Networks

  • Ghanshyam SinghEmail author
  • Shashank Gavel
  • Ajay Singh Raghuvanshi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 587)

Abstract

Clustering is a task which creates groups depending upon the presence of similarity between the data objects. Many clustering algorithms exist, which are capable of creating well-defined clusters. One of the popular algorithms is K-means, which is generally used for data clustering where performance is dependable on initial state of centroid but have limitation of trapping in local optima. Besides K-means, K-harmonic means, and Fuzzy C-means are also popular algorithms used for data clustering but again they have the same limitation of trapping in local optima. So this creates problem while handling anomaly existing dataset in wireless sensor network. In this paper, an analysis of best suitable hybrid clustering algorithm is brought for a congregation of normal and anomalous dataset by using a stochastic tool Particle Swarm Optimization (PSO) by utilizing different sensor datasets. The results are encouraging in terms of best suitable fitness function and low computational time.

Keywords

Wireless sensor network K-Means K-Harmonic means Fuzzy C-Means Particle swarm optimization 

References

  1. 1.
    Patcha, A., Park, J.M.: An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)CrossRefGoogle Scholar
  2. 2.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier (2011)Google Scholar
  3. 3.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  4. 4.
    Xia, Y., Wang, T., Zhao, R., Zhang, Y.: Image segmentation by clustering of spatial patterns. Pattern Recogn. Lett. 28(12), 1548–1555 (2007)CrossRefGoogle Scholar
  5. 5.
    Yang, S., Wu, R., Wang, M., Jiao, L.: Evolutionary clustering based vector quantization and SPIHT coding for image compression. Pattern Recogn. Lett. 31(13), 1773–1780 (2010)CrossRefGoogle Scholar
  6. 6.
    Liao, L., Lin, T., Li, B.: MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recogn. Lett. 29(10), 1580–1588 (2008)CrossRefGoogle Scholar
  7. 7.
    Sağlam, B., Salman, F.S., Sayın, S., Türkay, M.: A mixed-integer programming approach to the clustering problem with an application in customer segmentation. Eur. J. Oper. Res. 173(3), 866–879 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Moshtaghi, M., Havens, T.C., Bezdek, J.C., Park, L., Leckie, C., Rajasegarar, S., Palaniswami, M.: Clustering ellipses for anomaly detection. Pattern Recogn. 44(1), 55–69 (2011)CrossRefGoogle Scholar
  9. 9.
    Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer, Berlin, Heidelberg (2006)Google Scholar
  10. 10.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)CrossRefGoogle Scholar
  11. 11.
    Kao, Y.T., Zahara, E., Kao, I.W.: A hybridized approach to data clustering. Expert Syst. Appl. 34(3), 1754–1762 (2008)CrossRefGoogle Scholar
  12. 12.
    Cui, X., Potok, T. E., Palathingal, P.: Document clustering using particle swarm optimization. In: Proceedings IEEE of Swarm Intelligence Symposium, 2005, pp. 185–191 (2005)Google Scholar
  13. 13.
    Bezdek, J.C.: Fuzzy mathematics in pattern classification. Ph. D. Dissertation, Applied Mathematics, Cornell University (1973)Google Scholar
  14. 14.
    Pang, W., Wang, K., Zhou, C., Dong, L.: Fuzzy discrete particle swarm optimization for solving traveling salesman problem. In: Proceeding of Fourth International Conference on Computer and Information Technology, pp. 796–800. IEEE CS Press (1973)Google Scholar
  15. 15.
    Hamerly, G., Elkan, C.: Alternatives to the k-means algorithm that find better clusterings. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 600–607. ACM (2002)Google Scholar
  16. 16.
    Ünler, A., Güngör, Z.: Applying K-harmonic means clustering to the part-machine classification problem. Expert Syst. Appl. 36(2), 1179–1194 (2009)CrossRefGoogle Scholar
  17. 17.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers. Inc., San Francisco, CA (2001)Google Scholar
  18. 18.
    Izakian, H., Abraham, A.: Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst. Appl. 38(3), 1835–1838 (2011)CrossRefGoogle Scholar
  19. 19.
    Suthaharan, S., Alzahrani, M., Rajasegarar, S., Leckie, C., Palaniswami, M.: Labelled data collection for anomaly detection in wireless sensor networks. In: 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 269–274. IEEE (2010)Google Scholar
  20. 20.
    C.G.S.M.M.P. Bodik, P., Hong, W., Thibaux, R.: Ibrl dataset. http://db.csail.mit.edu/labdata/labdata.html
  21. 21.
    Huerta, R., Mosqueiro, T., Fonollosa, J., Rulkov, N.F., Rodriguez-Lujan, I.: Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring. Chemometr. Intell. Lab. Syst. 157, 169–176 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ghanshyam Singh
    • 1
    Email author
  • Shashank Gavel
    • 1
  • Ajay Singh Raghuvanshi
    • 1
  1. 1.Department of Electronics and TelecommunicationNational Institute of TechnologyRaipurIndia

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