scholarly journals Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging

Information ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Leonardo Rundo ◽  
Carmelo Militello ◽  
Giorgio Russo ◽  
Antonio Garufi ◽  
Salvatore Vitabile ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Prostate Gland ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique

2012 ◽  
Vol 4 (12) ◽  
pp. 32-40
Author(s):  
Satyendra Nath Mandal ◽  
Suhit Sinha ◽  
Saptarisha Chatterjee ◽  
Sankha Subhra Mullick ◽  
Sriparna Das
Keyword(s):  
Clustering Technique ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

An Evolutionary Neuro-Fuzzy C-means Clustering Technique

2020 ◽  
Vol 89 ◽  
pp. 103435 ◽  
Author(s):  
Priyanka D. Pantula ◽  
Srinivas S. Miriyala ◽  
Kishalay Mitra
Keyword(s):  
Clustering Technique ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES

2000 ◽  
Vol 08 (06) ◽  
pp. 735-746 ◽  
Author(s):  
ANNETTE KELLER ◽  
FRANK KLAWONN
Keyword(s):  
Objective Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Or Groups ◽  
Fuzzy C Means Clustering ◽  
Influence Parameter ◽  
Individual Variables ◽  
Single Data

We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.

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