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Fuzzy C-Means Clustering: Advances and Challenges (Part II)

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Janmenjoy Nayak

    (Maharaja Sriram Chandra Bhanja Deo (MSCB) University, Department of Computer Science)

  • H. Swapna Rekha

    (Aditya Institute of Technology and Management (AITAM), Department of Information Technology)

  • Bighnaraj Naik

    (Veer Surendra Sai University of Technology, Department of Computer Application)

Abstract

Undoubtedly, Fuzzy C-means (FCM) is considered as one of the most successful clustering algorithms since last two decades. It has been extensively used for solving many applications and been in limelight with better scope for more improvement as an accurate classifier. In this chapter, a brief review has been presented with the development and challenges of FCM in recent years. Mainly, the type of variations of FCM along with recent applications (mainly from the year 2015 to 2020) areas is discussed with a scope for further development. It is evident that, despite several developments in hard clustering like K-means, K-means+ +, etc., the applicability and algorithmic improvement of FCM has been on top position. Moreover, most of the developments on FCM are based on the improvement in the optimality condition of cluster fuzziness and its application in handling condensed data. Also, many works are developed on the issue of choosing the optimal cluster center in an effective way. Toward the end of this chapter, a factual analysis has been presented on the applications of FCM in various research domains with their growth.

Suggested Citation

  • Janmenjoy Nayak & H. Swapna Rekha & Bighnaraj Naik, 2023. "Fuzzy C-Means Clustering: Advances and Challenges (Part II)," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 239-269, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_12
    DOI: 10.1007/978-3-031-24628-9_12
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