Effective pattern taxonomy mining in text documents

Li, Yuefeng and Wu, Sheng-Tang and Tao, Xiaohui (2008) Effective pattern taxonomy mining in text documents. In: CIKM 2008: ACM 17th Conference on Information and Knowledge Management, 26-30 Oct 2008, Napa Valley, CA. United States.

Abstract

Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Poster)
Refereed: Yes
Item Status: Live Archive
Additional Information: Poster paper.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 01 Oct 2014 06:11
Last Modified: 01 Oct 2014 06:11
Uncontrolled Keywords: data mining techniques; existing method; pattern evolving; pattern taxonomy; research issues; text document; text mining
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0804 Data Format > 080403 Data Structures
08 Information and Computing Sciences > 0807 Library and Information Studies > 080704 Information Retrieval and Web Search
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1145/1458082.1458360
URI: http://eprints.usq.edu.au/id/eprint/20190

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