Dynamic Fluzzy Clustering Algorithm for Web Documents Mining

Qi Luo
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引用次数: 1

Abstract

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.
Web文档挖掘的动态模糊聚类算法
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。
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