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  • 标题:SELECTING CLASSIFICATION AND CLUSTERING TOOLS FOR ACADEMIC SUPPORT
  • 本地全文:下载
  • 作者:Manying Qiu
  • 期刊名称:Issues in Information Systems
  • 印刷版ISSN:1529-7314
  • 出版年度:2007
  • 卷号:8
  • 期号:2
  • 页码:265-272
  • 出版社:International Association for Computer Information Systems
  • 摘要:Classification and clustering are powerful and popular data mining techniques. Organizations use them to capture information, retain customers, and improve business performance. This paper presents a method for selecting data mining software for an academic environment based on its classification and clustering tools. This research applies the data mining software evaluation framework to evaluate three major commercial data mining software: SAS Enterprise Miner, Clementine from SPSS, and IBM DB2 Intelligent Miner. We added to the framework a criterion that became important in the Internet age. After ranking software on relevant criteria in the framework then purchase the best one that is affordable for academic support.
  • 关键词:Data mining; classification; clustering;software evaluation
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