Robust information gain based fuzzy c-means clustering and classification of carotid artery ultrasound images

2014 ◽  
Vol 113 (2) ◽  
pp. 593-609 ◽  
Author(s):  
Mehdi Hassan ◽  
Asmatullah Chaudhry ◽  
Asifullah Khan ◽  
M. Aksam Iftikhar
Keyword(s):  
Carotid Artery ◽  
Information Gain ◽  
Ultrasound Images ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Clustering And Classification

Metric-Based Semi-Supervised Fuzzy C-Means Clustering

2011 ◽  
Vol 268-270 ◽  
pp. 166-171
Author(s):  
Xue Song Yin ◽  
Qi Huang ◽  
Liang Ming Li
Keyword(s):  
Real World ◽  
Side Information ◽  
Data Sets ◽  
Membership Degree ◽  
Real World Data ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Clustering And Classification ◽  
Classification Tasks ◽  
Fuzzy C Means Algorithm

This paper presents a metric-based semi-supervised fuzzy c-means algorithm called MSFCM. Through using side information and unlabeled data together, MSFCM can be applied to both clustering and classification tasks. The resulting algorithm has the following advantages compared with semi-supervised clustering: firstly, membership degree as side information is used to guide the clustering of the data; secondly, through the metric learned, clustering accuracy can be greatly improved. Experimental results on a collection of real-world data sets demonstrated the effectiveness of the proposed algorithm.

scholarly journals Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

2013 ◽  
Vol 26 (6) ◽  
pp. 1071-1081 ◽  
Author(s):  
Asmatullah Chaudhry ◽  
Mehdi Hassan ◽  
Asifullah Khan ◽  
Jin Young Kim
Keyword(s):  
Carotid Artery ◽  
Active Contour ◽  
Ultrasound Images

Fuzzy c-means clustering for steady state events classification of electrical signals

2016 ◽  
Author(s):  
Jesus Ivan Sanchez-Gomez ◽  
Luis Morales-Velazquez ◽  
Roque Alfredo Osornio-Rios ◽  
Emmanuel Guillen-Garcia
Keyword(s):  
Steady State ◽  
Electrical Signals ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering
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