Fast global fuzzy C-means clustering for ECG signal classification; [EKG i̇şaretlerini siniflamak için hizli global bulanik C-ortalama öbekleşme]
| dc.contributor.author | Koçyiǧit Y. | |
| dc.contributor.author | Kiliç I. | |
| dc.date.accessioned | 2025-04-10T11:15:31Z | |
| dc.date.available | 2025-04-10T11:15:31Z | |
| dc.date.issued | 2010 | |
| dc.description.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. | |
| dc.identifier.DOI-ID | 10.1109/SIU.2010.5651537 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/51080 | |
| dc.title | Fast global fuzzy C-means clustering for ECG signal classification; [EKG i̇şaretlerini siniflamak için hizli global bulanik C-ortalama öbekleşme] | |
| dc.type | Conference paper |