The fuzzy c-means (FCM) algorithm is one of the most frequently used clustering algorithms. The weighting exponent m is a parameter that greatly influences the performance of the FCM. But there has been no theoretical basis for selecting the proper weighting exponent in the literature. In this paper, we develop a new theoretical approach to selecting the weighting exponent in the FCM. Based on this approach, we reveal the relation between the stability of the fixed points of the FCM and the data set itself. This relation provides the theoretical basis for selecting the weighting exponent in the FCM. The numerical experiments verify the effectiveness of our theoretical conclusion.Automation & Control SystemsComputer Science, Artificial I...
We introduce in this paper a new formulation of the regularized fuzzy C-means (FCM) algorithm which ...
Fuzzy clustering algorithms are widely used in many fields, more and more research results have been...
In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition ...
The weighting exponent m is called the fuzzifier that can have influence on the clustering performan...
Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail mark...
Abstract—Fuzzy C-means (FCM) is a powerful clustering algorithm and has been introduced to overcome ...
Fuzzy C-means (FCM) is a powerful clustering algorithm and has been introduced to overcome the crisp...
Several clustering algorithms include one or more parameters to be fixed before its application. Thi...
Fuzzy c-means is a well known fuzzy clustering al-gorithm. It is an unsupervised clustering algorith...
Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition....
Fuzzy C-Means (FCM) is a data clustering technique where the existence of each data point in a clust...
We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which ...
The fuzzy clustering algorithm has been widely used in the research area and production and life. Ho...
[[abstract]]The popular fuzzy c-means algorithm (FCM) is an objective function based clustering meth...
Abstract:-Fuzzy C-Means (FCM) clustering algorithm is used in a variety of application domains. Fund...
We introduce in this paper a new formulation of the regularized fuzzy C-means (FCM) algorithm which ...
Fuzzy clustering algorithms are widely used in many fields, more and more research results have been...
In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition ...
The weighting exponent m is called the fuzzifier that can have influence on the clustering performan...
Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail mark...
Abstract—Fuzzy C-means (FCM) is a powerful clustering algorithm and has been introduced to overcome ...
Fuzzy C-means (FCM) is a powerful clustering algorithm and has been introduced to overcome the crisp...
Several clustering algorithms include one or more parameters to be fixed before its application. Thi...
Fuzzy c-means is a well known fuzzy clustering al-gorithm. It is an unsupervised clustering algorith...
Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition....
Fuzzy C-Means (FCM) is a data clustering technique where the existence of each data point in a clust...
We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which ...
The fuzzy clustering algorithm has been widely used in the research area and production and life. Ho...
[[abstract]]The popular fuzzy c-means algorithm (FCM) is an objective function based clustering meth...
Abstract:-Fuzzy C-Means (FCM) clustering algorithm is used in a variety of application domains. Fund...
We introduce in this paper a new formulation of the regularized fuzzy C-means (FCM) algorithm which ...
Fuzzy clustering algorithms are widely used in many fields, more and more research results have been...
In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition ...