標題: Mining fuzzy frequent itemsets for hierarchical document clustering
作者: Chen, Chun-Ling
Tseng, Frank S. C.
Liang, Tyne
資訊工程學系
Department of Computer Science
關鍵字: Fuzzy association rule mining;Text mining;Hierarchical document clustering;Frequent itemsets
公開日期: 1-三月-2010
摘要: As text documents are explosively increasing in the Internet, the process of hierarchical document clustering has been proven to be useful for grouping similar documents for versatile applications. However, most document clustering methods still suffer from challenges in dealing with the problems of high dimensionality, scalability, accuracy, and meaningful cluster labels. In this paper, we will present an effective Fuzzy Frequent Item-set-Based Hierarchical Clustering (F(2)IHC) approach, which uses fuzzy association rule mining algorithm to improve the clustering accuracy of Frequent Item-set-Based Hierarchical Clustering (FIHC) method, In our approach, the key terms will be extracted from the document set, and each document is pre-processed into the designated representation for the following mining process. Then, a fuzzy association rule mining algorithm for text is employed to discover a set of highly-related fuzzy frequent itemsets, which contain key terms to be regarded as the labels of the candidate clusters. Finally, these documents will be clustered into a hierarchical cluster tree by referring to these candidate clusters. We have conducted experiments to evaluate the performance based on Classic4, Hitech, ReO, Reuters, and Wap datasets. The experimental results show that our approach not only absolutely retains the merits of FIHC, but also improves the accuracy quality of FIHC. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.ipm.2009.09.009
http://hdl.handle.net/11536/5765
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2009.09.009
期刊: INFORMATION PROCESSING & MANAGEMENT
Volume: 46
Issue: 2
起始頁: 193
結束頁: 211
顯示於類別:期刊論文


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  1. 000275611200007.pdf