Paper Info
Title
A dynamic genetic clustering algorithm for automatic choice of the number of clusters
Abstract
One of the most difficult problems in cluster analysis is how many clusters are appropriate for the description of a given system. In this paper, a novel dynamic genetic clustering algorithm (DGCA) is proposed to automatically search for the best number of clusters and the corresponding partitions. In the DGCA, a maximum attribute range partition approach is used in the population initialization in order to overcome the sensitivity of clustering algorithms to initial partitions. Furthermore, the methods of two-step selection and mutation operations are developed to exploit the search capability of the algorithm. Finally, the comparison among the DGCA, k-means algorithm and the standard genetic k-means clustering algorithm (SGKC) is illustrated with several artificial and real life data sets.
Year
DOI
Venue
2011
10.1109/ICCA.2011.6137921
ICCA
Keywords
Field
DocType
two-step selection operation,automatic choice,sensitivity,pattern clustering,maximum attribute range partition approach,dgca,statistical analysis,dynamic genetic clustering algorithm,population initialization,search capability,search problems,cluster analysis,mutation operation,genetic algorithms,standard genetic k-mean clustering algorithm,automatical search,k means clustering,k means algorithm,algorithm design,heuristic algorithm,indexation,genetics,algorithm design and analysis,genetic algorithm,clustering algorithms,indexes
Data mining,Fuzzy clustering,CURE data clustering algorithm,Computer science,Control theory,Artificial intelligence,Cluster analysis,Single-linkage clustering,Canopy clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Determining the number of clusters in a data set
Conference
Volume
Issue
ISSN
null
null
1948-3449
ISBN
Citations 
PageRank 
978-1-4577-1475-7
0
0.34
References 
Authors
12
2
Name
Order
Citations
PageRank
Hong He183.09
Yong-Hong Tan219935.68