Enhancing decision support for pattern classification via fuzzy entropy based fuzzy C-Means clustering

2013 ◽  
Author(s):  
Zhengmao Ye ◽  
Habib Mohamadian
Keyword(s):  
Decision Support ◽  
Pattern Classification ◽  
Fuzzy Entropy ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 554 ◽  
Author(s):  
Barbara Cardone ◽  
Ferdinando Di Martino
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Fuzzy Entropy ◽  
Entropy Function ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Random Initialization ◽  
Fuzzy C Means Algorithm ◽  
Optimal Cluster

One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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