TY - GEN
T1 - Memetic algorithm based fuzzy clustering
AU - Do, Anh Duc
AU - Cho, Siu Yeung
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.
AB - This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.
UR - http://www.scopus.com/inward/record.url?scp=79955344486&partnerID=8YFLogxK
U2 - 10.1109/CEC.2007.4424771
DO - 10.1109/CEC.2007.4424771
M3 - Conference contribution
AN - SCOPUS:79955344486
SN - 1424413400
SN - 9781424413409
T3 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
SP - 2398
EP - 2404
BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
Y2 - 25 September 2007 through 28 September 2007
ER -