posted on 2004-01-01, 00:00authored byS T Wu, Yuefeng Li, Y Xu, B Pham, Yi-Ping Phoebe Chen
In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.
History
Pagination
242 - 248
Location
Beijing, China
Open access
Yes
Start date
2004-09-20
End date
2004-09-24
ISBN-13
9780769521008
ISBN-10
0769521002
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Editor/Contributor(s)
N Zhong, H Tirri, Y Yao, L Zhou, J Liu, N Cercone
Title of proceedings
IEEE/WIC International Conference on Web Intelligence (WI 2004) : Beijing, China, September 20-24, 2004 : proceedings