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- W2386627105 abstract "In view of the characteristics of high dimensional spatial data and in order to reduce the curse of dimensionality effect on clustering results,this paper proposed and implemented a new sub-space clustering algorithm based on genetic algorithm,with the feature-choice method and the combination with global searching ability of genetic algorithm to search all of the feature sub-spaces.A real number system encoding method is adopted to encode the solution space,and a fitness evaluation function based on the distance and information entropy is designed to carry on evaluation on the clustering results and the characteristic dimension out of sub-space.Finally,a series of experiments of artificial data and real data were used to verify the high-efficiency and robustness of the algorithm.The results demonstrate that the new proposed algorithm can effectively carry out the high-dimensional data clustering and reduce the influence on the curse of dimensionality effect." @default.
- W2386627105 created "2016-06-24" @default.
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- W2386627105 date "2013-01-01" @default.
- W2386627105 modified "2023-09-27" @default.
- W2386627105 title "Research on high dimensional sub-space clustering algorithm based on genetic algorithm" @default.
- W2386627105 hasPublicationYear "2013" @default.
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