Web文档挖掘的动态模糊聚类算法

Qi Luo
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引用次数: 1

摘要

本文首先研究了web文档挖掘和文本聚类的方法,总结了模糊聚类算法和相似度度量函数,然后提出了一种改进的相似度函数,该函数可以解决高维空间的特征选择和特征提取问题。最后,将所提出的相似函数与近似的c -介质相结合,提出了一种动态模糊聚类算法(DCFCM)。实验表明,DCFCM可以有效地提高web文档聚类的精度,该方法在web文档挖掘中是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Fluzzy Clustering Algorithm for Web Documents Mining
This paper first studies the methods of web documents mining and text clustering, and summaries the fuzzy clustering algorithms and similarity measure functions, then proposes a modified similarity function which can solve the problems of feature selection and feature extraction in high-dimensional space. Finally, this paper puts forward to a dynamic fluzzy clustering algorithm(DCFCM) by combining the proposed similarity function with approximated C-mediods. The experiments show that DCFCM can effectively improve he precision of web documents clustering, the method is feasible in web documents mining.
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