Accelerated fuzzy C-means clustering algorithm

Doron Hershfinkel; Its'hak Dinstein

Accelerated fuzzy C-means clustering algorithm

Doron Hershfinkel, Its'hak Dinstein

Department of Electrical & Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 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 language	English
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Editors	Bruno Bosacchi, James C. Bezdek
Pages	41-52
Number of pages	12
State	Published - 1 Jan 1996
Event	Applications of Fuzzy Logic Technology III - Orlando, FL, USA Duration: 10 Apr 1996 → 12 Apr 1996

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2761

Conference

Conference	Applications of Fuzzy Logic Technology III
City	Orlando, FL, USA
Period	10/04/96 → 12/04/96

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Cite this

@inproceedings{07ce75933c92499d89b60dd1e16f8c8f,

title = "Accelerated fuzzy C-means clustering algorithm",

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.",

author = "Doron Hershfinkel and Its'hak Dinstein",

year = "1996",

month = jan,

day = "1",

language = "English",

isbn = "0819421421",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

pages = "41--52",

editor = "Bruno Bosacchi and Bezdek, {James C.}",

booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

note = "Applications of Fuzzy Logic Technology III ; Conference date: 10-04-1996 Through 12-04-1996",

}

Accelerated fuzzy C-means clustering algorithm. / Hershfinkel, Doron; Dinstein, Its'hak.
Proceedings of SPIE - The International Society for Optical Engineering. ed. / Bruno Bosacchi; James C. Bezdek. 1996. p. 41-52 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 2761).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Accelerated fuzzy C-means clustering algorithm

AU - Hershfinkel, Doron

AU - Dinstein, Its'hak

PY - 1996/1/1

Y1 - 1996/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0029747684&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0029747684

SN - 0819421421

SN - 9780819421425

T3 - Proceedings of SPIE - The International Society for Optical Engineering

SP - 41

EP - 52

BT - Proceedings of SPIE - The International Society for Optical Engineering

A2 - Bosacchi, Bruno

A2 - Bezdek, James C.

T2 - Applications of Fuzzy Logic Technology III

Y2 - 10 April 1996 through 12 April 1996

ER -

Accelerated fuzzy C-means clustering algorithm

Abstract

Publication series

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this