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Optimization in Fuzzy Clustering: A Review

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ICT with Intelligent Applications ( ICTIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 719))

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Abstract

Fuzzy clustering effectively handles the problem of mystically separable data through fuzzy partitioning. Popularly known as soft clustering, fuzzy clustering is based on membership degree of each data point. Various fuzzy clustering algorithms have been proposed in the literature. These algorithms work well in lower dimensions but are unable to find the global optimum in higher dimension. This problem has been solved by hybridizing the fuzzy clustering algorithms with various optimization algorithms. In this paper, we have reviewed four fuzzy clustering algorithms FCM, KFCM, IFCM, and KIFCM that have been optimized by hybridizing with various metaheuristic algorithms PSO, GA, FA, ACO, and ABC to further improve their performance.

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Authors and Affiliations

  1. USICT, Guru Gobind Singh Indraprastha University, Delhi, India

    Kanika Bhalla & Anjana Gosain

Authors
  1. Kanika Bhalla
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  2. Anjana Gosain
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Corresponding author

Correspondence to Kanika Bhalla .

Editor information

Editors and Affiliations

  1. Hertfordshire Business School, University of Hertfordshire, Hatfield, Hertfordshire, UK

    Jyoti Choudrie

  2. Department of AI and DS, Vishwakarma Institute of Information Technology, Pune, India

    Parikshit N. Mahalle

  3. Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia

    Thinagaran Perumal

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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Bhalla, K., Gosain, A. (2023). Optimization in Fuzzy Clustering: A Review. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT with Intelligent Applications. ICTIS 2023. Lecture Notes in Networks and Systems, vol 719. Springer, Singapore. https://doi.org/10.1007/978-981-99-3758-5_30

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