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- W2017033758 abstract "This paper studies the characteristics and key technology of Web text mining, and puts forward an improved DBSCAN density clustering algorithm.The algorithm combines the characteristics of hierarchical clustering effectively, it can confirmed class center well,and make the neighborhood parameter r self-adapt to the data sets with different density. To the data sets with different density, it can adjust parameters according to the dense degree. Simulation experiment results verify the proposed algorithm can improve the accuracy in the Web text mining. Keywords-Web text mining; DBSCAN; hierarchical clustering; self-adapt" @default.
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- W2017033758 date "2013-01-01" @default.
- W2017033758 modified "2023-10-16" @default.
- W2017033758 title "Application of An Improved DBSCAN Algorithm in Web Text Mining" @default.
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- W2017033758 doi "https://doi.org/10.2991/ccis-13.2013.92" @default.
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