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文章基本信息

  • 标题:A Propound Method for the Improvement of Cluster Quality
  • 本地全文:下载
  • 作者:Shveta Kundra Bhatia ; V. S. Dixit
  • 期刊名称:International Journal of Computer Science Issues
  • 印刷版ISSN:1694-0784
  • 电子版ISSN:1694-0814
  • 出版年度:2012
  • 卷号:9
  • 期号:4
  • 出版社:IJCSI Press
  • 摘要:In this paper Knockout Refinement Algorithm (KRA) is proposed to refine original clusters obtained by applying SOM and K-Means clustering algorithms. KRA Algorithm is based on Contingency Table concepts. Metrics are computed for the Original and Refined Clusters. Quality of Original and Refined Clusters are compared in terms of metrics. The proposed algorithm (KRA) is tested in the educational domain and results show that it generates better quality clusters in terms of improved metric values.
  • 关键词:Web Usage Mining; K;Means; Self Organizing Maps; Knockout Refinement Algorithm (KRA); Davies Bouldin (DB) Index; Dunns Index; Precision; Recall; F;Measure
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