Paper Info
Title
A Weighted Sum Validity Function for clustering with a Hybrid Niching Genetic Algorithm.
Abstract
Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.
Year
DOI
Venue
2005
10.1109/TSMCB.2005.850173
IEEE Transactions on Systems, Man, and Cybernetics, Part B
Keywords
Field
DocType
pattern clustering,cluster validity,evolutionary computation,proper number,hybrid niching genetic algorithm,objective function,niching method,related genetic clustering algorithm,k-means algorithm,genetic clustering algorithms,weighted sum validity function,wsvf objective function,optimization,normalized cluster validity functions,genetic algorithms,clustering solution,niching methods,appropriate partitioning,adequate objective function,clustering,evolutionary computing,genetic algorithm,indexing terms,genetics
Convergence (routing),Cluster (physics),Population,Mathematical optimization,Normalization (statistics),Correlation clustering,Computer science,Evolutionary computation,Artificial intelligence,Cluster analysis,Genetic algorithm,Machine learning
Journal
Volume
Issue
ISSN
35
6
1083-4419
Citations 
PageRank 
References 
36
1.73
26
Authors
4
Name
Order
Citations
PageRank
Weiguo Sheng125724.10
Stephen Swift242731.32
Leishi Zhang321918.17
Xiaohui Liu45042269.99