Fuzzy C-means clustering algorithm with multiple fuzzification coefficients

被引：0

作者：

Khang T.D. ^{[1
]}

Vuong N.D. ^{[1
]}

Tran M.-K. ^{[2
]}

Fowler M. ^{[2
]}

机构：

[1] Department of Information Systems, Hanoi University of Science and Technology, Hanoi

[2] Department of Chemical Engineering, University ofWaterloo, Waterloo, N2L 3G1, ON

来源：

Algorithms | 2020年 / 13卷 / 13期

关键词：

Clustering efficiency; Clustering technique; Fuzzification coefficient; Fuzzy C-means clustering; Fuzzy clustering; Machine learning; Objective function; Performance indices;

D O I：

10.3390/A13070158

中图分类号：

学科分类号：

摘要：

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. © 2020 by the authors.

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