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基于非結構化數據挖掘結構模型的Web文本聚類算法

Web text clustering algorithm based on the nonstructural data mining model

  • 摘要: 在非結構化數據挖掘結構模型——發現特征子空間模型(DFSSM)——的運行機制下,提出了一種新的Web文本聚類算法——基于DFSSM的Web文本聚類(WTCDFSSM)算法.該算法具有自穩定性,無須外界給出評價函數;能夠識別概念空間中最有意義的特征,抗噪聲能力強.結合現代遠程教育網應用背景實現了WTCDFSSM聚類算法.結果表明:該算法可以對各類遠程教育站點上收集的文本資料信息自動進行聚類挖掘;采用網格結構模型,幫助人們進行文本信息導航;從海量文本信息源中快速有效地獲取重要的知識.

     

    Abstract: Under the background of the nonstructural data mining model, a Web text clustering mining algorithm based on the discovery feature sub-space model (DFSSM), W, TCDFSSM algorithm, was proposed, which can distinguish the most meaningful features from the concept space without any evaluation function. The WTCDFSSM algorithm was applied in the modern long-distance education net. The result shows that it can automatically congregate the text information of education field, which is collected from education sites on Internet, help people to browse the important information quickly by information navigation mechanism and acquire useful knowledge.

     

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