Fast global fuzzy C-means clustering for ECG signal classification; [EKG i̇şaretlerini siniflamak için hizli global bulanik C-ortalama öbekleşme]
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Date
2010
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Abstract
Fuzzy clustering plays an important role in solving problems in the areas of pattern recognition and fuzzy model identification. The Fuzzy C-Means algorithm is one of widely used algorithms. It is based on optimizing an objective function, being responsive to initial conditions; the algorithm usually leads to local minimum results. Aiming at above problem, the fast global Fuzzy C-Means clustering algorithm (FGFCM) has been proposed, which is an incremental approach to clustering, and does not depend on any initial conditions. The algorithm was applied on ECG signals to classification. ©2010 IEEE.
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Keywords
Copying , Electrocardiography , Electrochromic devices , Fuzzy clustering , Fuzzy systems , Pattern recognition , Signal processing , ECG signals , Fuzzy C means clustering , Fuzzy C-means algorithms , Fuzzy c-means clustering algorithms , Fuzzy model identification , Incremental approach , Initial conditions , Local minimums , Objective functions , Clustering algorithms