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Experimental Analysis of Fuzzy Clustering Algorithms

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Intelligent Data Engineering and Analytics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

Abstract

Fuzzy clustering is an unsupervised technique for partitioning data into fuzzy clusters. Fuzzy clustering has wide applications in various domains of science and technology. So, in this paper, we have drawn a performance comparison of five fuzzy clustering algorithms: FCM, PFCM, CFCM, IFCM, and NC. Their performance is analyzed on the bases of cluster homogeneity, clusters varying in size, shape, and density as well as when population of outliers increases. Four standard datasets: D12, D15, Dunn, and Noisy Dunn are used for this review work. This research paper will be very helpful to researchers to choose the right algorithm as per the features of their data clusters.

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References

  1. Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005)

    Article  Google Scholar 

  2. Chintalapudi, K.K., Kam M.: The credibilistic fuzzy c means clustering algorithm. In: 1998 IEEE International Conference on Systems, Man, and Cybernetics, 1998, vol. 2, pp. 2034–2039. IEEE (1998)

    Google Scholar 

  3. Xu, Z., Junjie, W.: Intuitionistic fuzzy C-means clustering algorithms. J. Syst. Eng. Electron. 21(4), 580–590 (2010)

    Article  Google Scholar 

  4. Panda, S., Sahu, S., Jena, P. and Chattopadhyay, S.: Comparing fuzzy-C means and K-means clustering techniques: a comprehensive study. In: Advances in Computer Science, Engineering & Applications, pp. 451–460. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  5. Gosain, A., Dahiya, S.: Performance analysis of various fuzzy clustering algorithms: a review. Procedia Comput. Sci. 79, 100–111 (2016)

    Article  Google Scholar 

  6. Ayed, A.B., Halima, M.B., Alimi, A.M.: Survey on clustering methods: Towards fuzzy clustering for big data. In: 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 331–336. IEEE (2014)

    Google Scholar 

  7. Li, J., Lewis, H.W.: Fuzzy clustering algorithms—review of the applications. In: 2016 IEEE International Conference on Smart Cloud (SmartCloud), pp. 282–288. IEEE (2016)

    Google Scholar 

  8. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)

    Article  Google Scholar 

  9. Keller, A.: Fuzzy clustering with outliers. In: Fuzzy Information Processing Society, 2000. NAFIPS. 19th International Conference of the North American, pp. 143–147. IEEE (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. CSE, Delhi Technological University, Delhi, India

    Sonika Dahiya

  2. Maharaja Surajmal Institute of Technology, Delhi, India

    Anushika Gosain & Suman Mann

Authors
  1. Sonika Dahiya
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  2. Anushika Gosain
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  3. Suman Mann
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Corresponding author

Correspondence to Sonika Dahiya .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute Industrial Technology, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Management, National Institute of Technology Karnataka, Surathkal, Karnataka, India

    Ritanjali Majhi

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dahiya, S., Gosain, A., Mann, S. (2021). Experimental Analysis of Fuzzy Clustering Algorithms. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_29

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