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Open AccessArticle

Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

1
Department of Information Systems, Hanoi University of Science and Technology, Hanoi 10000, Vietnam
2
Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
*
Authors to whom correspondence should be addressed.
Algorithms 2020, 13(7), 158; https://doi.org/10.3390/a13070158
Received: 7 June 2020 / Revised: 24 June 2020 / Accepted: 28 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Supervised and Unsupervised Classification Algorithms)
Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods. View Full-Text
Keywords: clustering technique; fuzzy clustering; fuzzy C-means clustering; fuzzification coefficient; objective function; performance indices; clustering efficiency; machine learning clustering technique; fuzzy clustering; fuzzy C-means clustering; fuzzification coefficient; objective function; performance indices; clustering efficiency; machine learning
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Khang, T.D.; Vuong, N.D.; Tran, M.-K.; Fowler, M. Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients. Algorithms 2020, 13, 158.

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