基于非结构化数据挖掘结构模型的Web文本聚类算法

在非结构化数据挖掘结构模型——发现特征子空间模型(DFSSM)——的运行机制下,提出了一种新的Web文本聚类算法——基于DFSSM的Web文本聚类(WTCDFSSM)算法.该算法具有自稳定性,无须外界给出评价函数;能够识别概念空间中最有意义的特征,抗噪声能力强.结合现代远程教育网应用背景实现了WTCDFSSM聚类算法.结果表明:该算法可以对各类远程教育站点上收集的文本资料信息自动进行聚类挖掘;采用网格结构模型,帮助人们进行文本信息导航;从海量文本信息源中快速有效地获取重要的知识.

Web text clustering algorithm based on the nonstructural data mining model

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.