Paper Info
Title
Clonal selection based fuzzy C-means algorithm for clustering
Abstract
In recent years, fuzzy based clustering approaches have shown to outperform state-of-the-art hard clustering algorithms in terms of accuracy. The difference between hard clustering and fuzzy clustering is that in hard clustering each data point of the data set belongs to exactly one cluster, and in fuzzy clustering each data point belongs to several clusters that are associated with a certain membership degree. Fuzzy c-means clustering is a well-known and effective algorithm, however, the random initialization of the centroids directs the iterative process to converge to local optimal solutions easily. In order to address this issue a clonal selection based fuzzy c-means algorithm (CSFCM) is introduced. CSFCM is compared with the basic Fuzzy C-Means (FCM) algorithm, a genetic algorithm based FCM (GAFCM) algorithm, and a particle swarm optimization based FCM (PSOFCM) algorithm.
Year
DOI
Venue
2014
10.1145/2576768.2598270
GECCO
Keywords
Field
DocType
data clustering,evolutionary computation,fuzzy c-means algorithm,heuristic methods
Fuzzy clustering,CURE data clustering algorithm,Computer science,FLAME clustering,Artificial intelligence,Cluster analysis,Single-linkage clustering,Canopy clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Algorithm,Machine learning
Conference
Citations 
PageRank 
References 
2
0.39
21
Authors
1
Name
Order
Citations
PageRank
Simone A Ludwig11309179.41