KMS Of Academy of mathematics and systems sciences, CAS
Text clustering using frequent itemsets | |
Zhang, Wen1; Yoshida, Taketoshi3; Tang, Xijin2; Wang, Qing1 | |
2010-07-01 | |
发表期刊 | KNOWLEDGE-BASED SYSTEMS |
ISSN | 0950-7051 |
卷号 | 23期号:5页码:379-388 |
摘要 | Frequent itemset originates from association rule mining. Recently, it has been applied in text mining such as document categorization, clustering, etc. In this paper, we conduct a study on text clustering using frequent itemsets. The main contribution of this paper is three manifolds. First, we present a review on existing methods of document clustering using frequent patterns. Second, a new method called Maximum Capturing is proposed for document clustering. Maximum Capturing includes two procedures: constructing document clusters and assigning cluster topics. We develop three versions of Maximum Capturing based on three similarity measures. We propose a normalization process based on frequency sensitive competitive learning for Maximum Capturing to merge cluster candidates into predefined number of clusters. Third, experiments are carried out to evaluate the proposed method in comparison with CFWS, CMS, FTC and FIHC methods. Experiment results show that in clustering, Maximum Capturing has better performances than other methods mentioned above. Particularly, Maximum Capturing with representation using individual words and similarity measure using asymmetrical binary similarity achieves the best performance. Moreover, topics produced by Maximum Capturing distinguished clusters from each other and can be used as labels of document clusters. (C) 2010 Elsevier B.V. All rights reserved. |
关键词 | Document clustering Frequent itemsets Maximum capturing Similarity measure Competitive learning |
DOI | 10.1016/j.knosys.2010.01.011 |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[90718042] ; National Natural Science Foundation of China[60873072] ; National Natural Science Foundation of China[60903050] ; National Hi-Tech RD Plan of China[2007AA010303] ; National Hi-Tech RD Plan of China[2007AA01Z186] ; National Hi-Tech RD Plan of China[2007AA01Z179] ; National Basic Research Program[2007CB310802] ; Foundation of Young Doctors of Institute of Software, Chinese Academy of Sciences[ISCAS2009-DR03] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000278881300002 |
出版者 | ELSEVIER SCIENCE BV |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.amss.ac.cn/handle/2S8OKBNM/10220 |
专题 | 系统科学研究所 |
通讯作者 | Zhang, Wen |
作者单位 | 1.Chinese Acad Sci, Inst Software, Lab Internet Software Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.Japan Adv Inst Sci & Technol, Sch Knowledge Sci, Tatsunokuchi, Ishikawa 9231292, Japan |
推荐引用方式 GB/T 7714 | Zhang, Wen,Yoshida, Taketoshi,Tang, Xijin,et al. Text clustering using frequent itemsets[J]. KNOWLEDGE-BASED SYSTEMS,2010,23(5):379-388. |
APA | Zhang, Wen,Yoshida, Taketoshi,Tang, Xijin,&Wang, Qing.(2010).Text clustering using frequent itemsets.KNOWLEDGE-BASED SYSTEMS,23(5),379-388. |
MLA | Zhang, Wen,et al."Text clustering using frequent itemsets".KNOWLEDGE-BASED SYSTEMS 23.5(2010):379-388. |
条目包含的文件 | 条目无相关文件。 |
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