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Effective Pattern Discovery for Text Mining
January 2012 (vol. 24 no. 1)
pp. 30-44
ASCII Text
x 
 
Ning Zhong, Yuefeng Li, Sheng-Tang Wu, "Effective Pattern Discovery for Text Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 30-44, January, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.211,
author = {Ning Zhong and Yuefeng Li and Sheng-Tang Wu},
title = {Effective Pattern Discovery for Text Mining},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {1},
issn = {1041-4347},
year = {2012},
pages = {30-44},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.211},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Effective Pattern Discovery for Text Mining
IS - 1
SN - 1041-4347
SP30
EP44
EPD - 30-44
A1 - Ning Zhong,
A1 - Yuefeng Li,
A1 - Sheng-Tang Wu,
PY - 2012
KW - Text mining
KW - text classification
KW - pattern mining
KW - pattern evolving
KW - information filtering.
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Ning Zhong, Maebashi Institute of Technology, Maebashi
Yuefeng Li, Queensland University of Technology, Brisbane
Sheng-Tang Wu, Asia University, Taiwan
Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

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Index Terms:
Text mining, text classification, pattern mining, pattern evolving, information filtering.
Citation:
Ning Zhong, Yuefeng Li, Sheng-Tang Wu, "Effective Pattern Discovery for Text Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 30-44, Jan. 2012, doi:10.1109/TKDE.2010.211
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