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Fuzzy Clustering with Improved Swarm Optimization and Genetic Algorithm: Hybrid Approach

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Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 556))

Abstract

Fuzzy c-means clustering is one of the popularly used algorithms in various diversified areas of applications due to its ease of implementation and suitability of parameter selection, but it suffers from one major limitation like easy stuck at local optima positions. Particle swarm optimization is a globally adopted metaheuristic technique used to solve complex optimization problems. However, this technique needs a lot of fitness evaluations to get the desired optimal solution. In this paper, hybridization between the improved particle swarm optimization and genetic algorithm has been performed with fuzzy c-means algorithm for data clustering. The proposed method has been compared with some of the existing algorithms like genetic algorithm, PSO, and K-means method. Simulation result shows that the proposed method is efficient and can divulge encouraging results for finding global optimal solutions.

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References

  1. Izakian, Hesam, and Ajith Abraham. Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications 38.3 (2011): 1835–1838.

    Google Scholar 

  2. Zadeh, L.A.: Fuzzy Sets. Information and Control 8 (3): 338–353. doi:10.1016/S0019-9958(65)90241-X.ISSN 0019–9958.

  3. Bellman, R.E., Kalaba, R.A., Zadeh, L.A.: Abstraction and pattern classification, J. Math. Anal. Appl. 13 (1966) 1–7.

    Google Scholar 

  4. Ruspini, E.H.: A new approach to clustering, Inf. Control 15(1) (1969) 22–32.

    Google Scholar 

  5. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981).

    Google Scholar 

  6. Ferreiraa, M.R.P., Carvalho, F.A.T.: Kernel fuzzy c-means with automatic variable weighting, Fuzzy Sets and Systems 237 (2014) 1–46.

    Google Scholar 

  7. F. Höppner, F. Klawonn, R. Kruse, T. Runkler, Fuzzy Cluster Analysis. John Wiley & Sons, Inc. (1999).

    Google Scholar 

  8. Khattab, Dina, et al. A Comparative Study of Different Color Space Models Using FCM-Based Automatic GrabCut for Image Segmentation. Computational Science and Its Applications–ICCSA 2015. Springer International Publishing, (2015) 489–501.

    Google Scholar 

  9. Kim, W.D., Lee, K.H., Lee, D.: A novel initialization scheme for the fuzzy c-means algorithm for color clustering, Pattern Recognition Letters 25 (2004) 227–237.

    Google Scholar 

  10. Lazaro J, Arias J, Martın J.L, Cuadrado C, Astarloa A.: Implementation of a modified Fuzzy C-Means clustering algorithm for real-time applications. Microprocessors and Microsystems 29 (2005) 375–380.

    Google Scholar 

  11. ŁeRski, J.M., Owczarek, A.J.: A time-domain-constrained fuzzy clustering method and its application to signal analysis. Fuzzy Sets and Systems 155 (2005) 165–190.

    Google Scholar 

  12. Inan, Z.H., Kuntalp, M.: A study on fuzzy C-means clustering-based systems in automatic spike detection, Computers in Biology and Medicine 37 (2007) 1160–1166.

    Google Scholar 

  13. Ceccarelli, M., Maratea, A.: Improving fuzzy clustering of biological data by metric learning with side information, International Journal of Approximate Reasoning 47 (2008) 45–57.

    Google Scholar 

  14. Chen, S.M., Chang, Y.C.: Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. Information Sciences 180 (2010) 4772–4783.

    Google Scholar 

  15. Azara, A.T., El-Said, S.A., Hassaniend, A.E.: Fuzzy and hard clustering analysis for thyroid disease, Computer methods and programs in biomedicine 111 (2013) 1–16.

    Google Scholar 

  16. Kaushik, A., Soni, A.K., Soni, R.: Radial basis function network using intuitionistic fuzzy C means for software cost estimation, Int. J. of Computer Applications in Technology 2013 - Vol. 47, No. 1 pp. 86–95.

    Google Scholar 

  17. Silva, S., Junior, M.D., Junior, V. L., Brennan, M.J.: Structural damage detection by fuzzy clustering, Mechanical Systems and Signal Processing 22 (2008) 1636–1649.

    Google Scholar 

  18. Yan, Y., Chen, L., Tjhi, W.C.: Fuzzy semi-supervised co-clustering for text documents, Fuzzy Sets and Systems 215 (2013) 74–89.

    Google Scholar 

  19. Nayak, J., Nanda, M., Nayak, K., Naik, B., Behera, H.S.: An Improved Firefly Fuzzy C-Means (FAFCM) Algorithm for Clustering Real World Data Sets, Smart Innovation, Systems and Technologies Volume 27, 2014, pp. 339–348, doi:10.1007/978-3-319-07353-8_40.

  20. Zhu, C.J., Yang, S., Zhao, Q., S., Cui, Wen, N.: Robust Semi-supervised Kernel-FCM Algorithm Incorporating Local Spatial Information for Remote Sensing Image Classification, Journal of the Indian Society of Remote Sensing, March 2014, Volume 42, Issue 1, pp. 35–49.

    Google Scholar 

  21. Yu, X.C., He, H., Hu, D., Zhou, W.: Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm, Science China Earth Sciences, June 2014, Volume 57, Issue 6, pp. 1306–1313, doi:10.1007/s11430-013-4689.

  22. Tang, Jinjun, et al. A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation. Transportation Research Part C: Emerging Technologies 51 (2015): 29–40.

    Google Scholar 

  23. Li Wang et. al. Particle Swarm Optimization for Fuzzy c-Means Clustering. Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21–23 (2006) Dalian, China pp. 6055–6058.

    Google Scholar 

  24. Thomas A. Runkler and Christina Katz. Fuzzy Clustering by Particle Swarm Optimization. 2006 IEEE International Conference on Fuzzy Systems. Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16–21 (2006) pp. 601–608.

    Google Scholar 

  25. Silva Filho, Telmo M., et al. Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Systems with Applications 42.17 (2015): 6315–6328.

    Google Scholar 

  26. 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 

  27. Nayak, Janmenjoy, Bighnaraj Naik, and H. S. Behera. Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014. Computational Intelligence in Data Mining-Volume 2. Springer India (2015) 133–149.

    Google Scholar 

  28. Asyali, M.H., Colak, D., Demirkaya, O., Inan, M.S.: Gene expression profile classification: a review. Current Bioinformatics 1 (2006) 55–73.

    Google Scholar 

  29. Phen-Lan Lin, Po-Whei Huang, C.H. Kuo, Y.H. Lai, A size-insensitive integrity-based fuzzy c-means method for data clustering. Pattern Recognition 47 (2014) 2042–2056.

    Google Scholar 

  30. Babaei, M.: A general approach to approximate solutions of nonlinear differential equations using particle swarm optimization. Applied Soft Computing 13 (2013) 3354–3365.

    Google Scholar 

  31. Neri, F., Mininno, E., Iacca, G.: Compact Particle Swarm Optimization. Information Sciences 239 (2013) 96–121.

    Google Scholar 

  32. Wei, J., Guangbin, L., Dong, L.: Elite particle swarm optimization with mutation, IEEE Asia Simulation Conference – 7th Intl. Conf. on Sys. Simulation and Scientific Computing. (2008) 800–803.

    Google Scholar 

  33. Khare, A., Rangnekar, S.: A review of particle swarm optimization and its applications in Solar Photovoltaic system. Applied Soft Computing. 13 (2013) 2997–3006.

    Google Scholar 

  34. Yue-bo, M., ZouJian-hua, GanXu-sheng, Liang, Z.: Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm. Neurocomputing 83 (2012) 212–221.

    Google Scholar 

  35. Dehuri, S., Roy, R., Cho, S. B., Ghosh, A.: An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. The Journal of Systems and Software. 85 (2012) 1333–1345.

    Google Scholar 

  36. Holland, J. H.: Adaption in Natural and Artificial Systems. Cambridge, MA: MIT Press, 1975.

    Google Scholar 

  37. Goldberg, D. E.: Genetic algorithms in search. Optimization and machine learning. Boston, MA: Kluwer Academic Publishers. 1989.

    Google Scholar 

  38. Li, X., Xiao, N., Claramunt, C., Lin, H.: Initialization strategies to enhancing the performance of genetic algorithms for the p-median problem. Computers & Industrial Engineering 61 (2011) 1024–1034.

    Google Scholar 

  39. Bache, K., Lichman, M.: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml], Irvine, CA: University of California, School of Information and Computer Science. 2013.

  40. 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. 2012 National Conference on Computing and Communication Systems (NCCCS) (2012) 1–5 doi:10.1109/NCCCS.2012.6413027.

  41. Nayak, J., Kanungo, D. P., Naik, B., & Behera, H. S. (2016). Evolutionary Improved Swarm-Based Hybrid K-Means Algorithm for Cluster Analysis. In Proceedings of the Second International Conference on Computer and Communication Technologies (pp. 343–352). Springer India.

    Google Scholar 

  42. 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, Springer India 545–553 (2015) doi:10.1007/978-81-322-2250-7_54.

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Acknowledgements

This work is supported by Technical Education Quality Improvement Programme, National Project Implementation Unit (A unit of MHRD, Govt. of India, for implementation of World Bank assisted projects in technical education), under the research project grant (VSSUT/TEQIP/37/2016).

Author information

Authors and Affiliations

  1. Department of Computer Application, VSSUT, Burla, Odisha, India

    Bighnaraj Naik

  2. Department of CSE & IT, ITER, S’O’A University, Bhubaneswar, Odisha, India

    Sarita Mahapatra

  3. Department of CSE, Modern Engineering & Management Studies, Balasore, Odisha, India

    Janmenjoy Nayak

  4. Department of CSE & IT, VSSUT, Burla, Odisha, India

    H. S. Behera

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  1. Bighnaraj Naik
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  2. Sarita Mahapatra
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  3. Janmenjoy Nayak
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  4. H. S. Behera
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Corresponding author

Correspondence to Janmenjoy Nayak .

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Editors and Affiliations

  1. Department of Computer Science and Engineering & Information Technology, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of CSE, National Institute of Technology (NIT), Rourkela, Odisha, India

    Durga Prasad Mohapatra

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Naik, B., Mahapatra, S., Nayak, J., Behera, H.S. (2017). Fuzzy Clustering with Improved Swarm Optimization and Genetic Algorithm: Hybrid Approach. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-10-3874-7_23

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  • DOI: https://doi.org/10.1007/978-981-10-3874-7_23

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