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《Journal of Zhejiang University(Engineering Science)》 2014-06
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Count adaptive clustering algorithm based on multiple-chromosome evolution

NI Guang-yi;ZHANG Xiao-can;SU Cheng;YU Wei-bin;Department of Geoscience,Zhejiang University;  
Two well-known problems exist when genetic algorithms(GAs)applied to data clustering:the"content-sensitivity"problem which restrains efficiency,and how to determine the count of clusters without prior knowledge.In order to resolve these problems,by fully researching on the nature of how"context-sensitivity"affects efficiency and convergency,a multiple chromosome genetic clustering algorithm was proposed,which takes the advantage of the evolvement of multiple chromosome creatures.The algorithm improved the efficiency and convergency,by making use of the interrelationship among all clusters while evolving,moreover,by this way the count of cluster is variable in each individual.To control the count of clusters,fitness functions which based on distribution fitting was defined,which can provide decision information without prior knowledge.The comparison of the algorithm with the K genetic algorithm(KGA)and the expectation-maximization(EM)algorithm and the results of experiments on remote sensing image show promising results on efficiency and convergency with cluster count automatically adjusted.
【CateGory Index】: TP18
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