An adapted spatial information kernel-based Fuzzy C-Means clustering method

2014 ◽  
Author(s):  
Zhe Liu ◽  
Yuqing Song
Keyword(s):  
Spatial Information ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

New fuzzy C-means clustering method based on feature-weight and cluster-weight learning

2019 ◽  
Vol 78 ◽  
pp. 324-345 ◽  
Author(s):  
Mahdi Hashemzadeh ◽  
Amin Golzari Oskouei ◽  
Nacer Farajzadeh
Keyword(s):  
Clustering Method ◽  
Fuzzy C Means ◽  
Feature Weight ◽  
Fuzzy C Means Clustering ◽  
Weight Learning

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

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.

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