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  • 标题:Efficient Incremental Clustering of Documents based on Correlation
  • 本地全文:下载
  • 作者:A.Devender ; B.Srinivas ; A.Ashok
  • 期刊名称:International Journal of Engineering and Computer Science
  • 印刷版ISSN:2319-7242
  • 出版年度:2015
  • 卷号:4
  • 期号:8
  • 页码:13704-13709
  • DOI:10.18535/ijecs/v4i8.15
  • 出版社:IJECS
  • 摘要:With this project, a few dynamic file clustering algorithms, namely: Term consistency based GreatestResemblance Doc Clustering (TMARDC), Correlated Concept primarily based MAximum Resemblance DocumentClustering (CCMARDC) and Correlated Notion based Quickly Incremental Clustering Criteria (CCFICA) usually areproposed. From the aforementioned three suggested algorithms this TMARDC algorithm will be based upon termconsistency, whereas, the CCMARDC and CCFICA are based on Correlated conditions (Terms and their Associatedterms) notion extraction protocol
  • 关键词:Clustering; Document Analysis
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