Fast global fuzzy C-means clustering for ECG signal classification; [EKG i̇şaretlerini siniflamak için hizli global bulanik C-ortalama öbekleşme]

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dc.contributor.authorKoçyiǧit Y.
dc.contributor.authorKiliç I.
dc.date.accessioned2024-07-22T08:20:37Z
dc.date.available2024-07-22T08:20:37Z
dc.date.issued2010
dc.description.abstractFuzzy 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.
dc.identifier.DOI-ID10.1109/SIU.2010.5651537
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18254
dc.language.isoTurkish
dc.subjectCopying
dc.subjectElectrocardiography
dc.subjectElectrochromic devices
dc.subjectFuzzy clustering
dc.subjectFuzzy systems
dc.subjectPattern recognition
dc.subjectSignal processing
dc.subjectECG signals
dc.subjectFuzzy C means clustering
dc.subjectFuzzy C-means algorithms
dc.subjectFuzzy c-means clustering algorithms
dc.subjectFuzzy model identification
dc.subjectIncremental approach
dc.subjectInitial conditions
dc.subjectLocal minimums
dc.subjectObjective functions
dc.subjectClustering algorithms
dc.titleFast global fuzzy C-means clustering for ECG signal classification; [EKG i̇şaretlerini siniflamak için hizli global bulanik C-ortalama öbekleşme]
dc.typeConference paper

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