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Fuzzy C-Means Hybrid with Fuzzy Bacterial Colony Optimization

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Advances in Electrical and Computer Technologies (ICAECT 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 711))

Abstract

In Data mining, Fuzzy or soft clustering is one of the popular approaches proposed to solve several real-world problems. The Fuzzy C-Means (FCM) algorithm is the famous algorithm in fuzzy clustering because of its straightforwardness and short computational effort. But it has the problem of local optima. To overcome this local optima problem, many optimization algorithms have been developed and try to attain a global optimum solution. In this research work, two kinds of enhancement are proposed to solve clustering problem and overcome the above-mentioned shortcomings. First, Bacterial Colony Optimization (BCO) algorithm is integrated with fuzzy theory called Fuzzy BCO (FBCO). Second, Hybridization of FCM with FBCO is developed to obtaining good optimal clusters are called as Hybridization of Fuzzy Clustering Algorithms (HFCA). The experimental results of proposed algorithms are demonstrated using six machine learning datasets and the results produced by proposed FBCO and HFCA generates higher performance while match up with FCM, FPSO (Fuzzy Particle Swarm Optimization) and FBFO (Fuzzy Bacterial Foraging Optimization) algorithms.

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Correspondence to K. Vijayakumari .

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Vijayakumari, K., Baby Deepa, V. (2021). Fuzzy C-Means Hybrid with Fuzzy Bacterial Colony Optimization. In: Sengodan, T., Murugappan, M., Misra, S. (eds) Advances in Electrical and Computer Technologies. ICAECT 2020. Lecture Notes in Electrical Engineering, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-15-9019-1_7

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  • DOI: https://doi.org/10.1007/978-981-15-9019-1_7

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  • Print ISBN: 978-981-15-9018-4

  • Online ISBN: 978-981-15-9019-1

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