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Research and Application of Combinatorial Clustering Methods in the Text Clustering
Author: FangChun
Tutor: HuJinZhu
School: Central China Normal University
Course: Applied Computer Technology
Keywords: Data Mining Combined Clustering Feature selection DSOM-FS-K-means algorithm DSOM-FS-FCM algorithm
CLC: TP391.1
Type: Master's thesis
Year: 2009
Downloads: 114
Quote: 5
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Abstract
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The age of the Internet , the amount of text in the web and the number of people accessing these documents have been massive increase of such a huge number of text messages , the people in order to find out the contents of a number of related topics , relying on manual classification methods have been unable to meet the actual needs . The use of computer to help us finishing WEB content and subsequent processing is a common means . The text clustering Research is one of data mining is very hot research topic . Study text clustering algorithm is mainly concentrated in a single cluster and its associated parameters improved , the focus of this study is the combination of clustering method . First analysis of the more popular text clustering clustering algorithm ( SOM clustering algorithm , K - means clustering algorithm FCM clustering algorithm ) , these three kinds of algorithms , a detailed description and analysis of their respective advantages and shortcomings. Then , the combination of the characteristics of the text feature selection method analysis of two combinations clustering process model theory shows its effectiveness and characteristics , and its corresponding clustering algorithm : DSOM-FS-K-means algorithm and DSOM-FS-FCM algorithm , which DSOM-FS-FCM algorithm also uses the optimization function to adjust the membership function of the FCM algorithm to reduce the impact of outlier data clustering effect . Finally, in order to verify the effectiveness of the combination of clustering algorithm , these two combinations of algorithms and each corresponding to a single clustering algorithm , and no combination of clustering algorithm combined with feature selection comparative analysis of the experimental results prove that the Combining the advantages of clustering algorithm .
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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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