Accelerated fuzzy C-means clustering algorithm

Doron Hershfinkel, Its'hak Dinstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

The proposed accelerated fuzzy c-means (AFCM) clustering algorithm is an improved version of the fuzzy c-mean (FCM) algorithm. Each iteration of the proposed algorithm consists of the regular operations of the FCM algorithm followed by an improvement stage. Once the cluster center locations are updated by the regular FCM algorithm operations, the improvement stage shifts each cluster center farther in its respective update direction. A number of possible strategies for the shift size control are studied and evaluated. The AFCM was applied to a number of data sets, using hundreds of different initial cluster center sets, yielding reductions of 37% to 65% in the number of iterations required for convergence by a similar FCM algorithm.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsBruno Bosacchi, James C. Bezdek
Pages41-52
Number of pages12
StatePublished - 1 Jan 1996
EventApplications of Fuzzy Logic Technology III - Orlando, FL, USA
Duration: 10 Apr 199612 Apr 1996

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2761

Conference

ConferenceApplications of Fuzzy Logic Technology III
CityOrlando, FL, USA
Period10/04/9612/04/96

Fingerprint

Dive into the research topics of 'Accelerated fuzzy C-means clustering algorithm'. Together they form a unique fingerprint.

Cite this