A Robust Algorithm for Fuzzy Document Clustering

Lifei Chen, Shengrui Wang, Q. Jiang
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引用次数: 6

Abstract

In many applications of document clustering, a document may include multiple topics and thus may relate to multiple categories at the same time. Most of the existing subspace clustering algorithms can only perform hard clustering on document collections. In this paper, a fuzzy algorithm named R-FPC is introduced for document clustering. The algorithm discovers soft partitions of a data set in the soft subspaces of the data space. Using the proposed R-Greedy initialization method, R-FPC can always generate stable clustering results with competitive accuracy. The experiments are conducted on some widely used corpuses and the results have shown effectiveness and robustness of the proposed methods.
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一种鲁棒的模糊文档聚类算法
在文档聚类的许多应用程序中,一个文档可能包含多个主题,因此可能同时与多个类别相关。现有的子空间聚类算法大多只能对文档集合进行硬聚类。本文提出了一种用于文档聚类的模糊算法R-FPC。该算法在数据空间的软子空间中发现数据集的软分区。采用本文提出的R-Greedy初始化方法,R-FPC总能生成稳定的聚类结果,且具有竞争精度。在一些广泛使用的语料库上进行了实验,结果表明了所提方法的有效性和鲁棒性。
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