Rough association rule mining in text documents for acquiring Web user information needs

Li, Yuefeng & Zhong, Ning (2006) Rough association rule mining in text documents for acquiring Web user information needs. In Nishida, T., Shi, Z., Visser, U., Wu, X., Liu, J., Wah, B., et al. (Eds.) WI 2006 Main Conference, 18-22 December, Hong Kong, China.

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It is a big challenge to apply data mining techniques for effective Web information gathering because of duplications and ambiguities of data values (e.g., terms). To provide an effective solution to this challenge, this paper first explains the relationship between association rules and rough set based decision rules. It proves that a decision pattern is a kind of closed pattern. It also presents a novel concept of rough association rules in order to improve the effectiveness of association rule mining. The premise of a rough association rule consists of a set of terms and a frequency distribution of terms. The distinct advantage of rough association rules is that they contain more specific information than normal association rules. It is also feasible to update rough association rules dynamically to produce effective results

Impact and interest:

8 citations in Scopus
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ID Code: 10164
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1109/WI.2006.151
ISBN: 0769527477
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2006 IEEE
Copyright Statement: 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.
Deposited On: 15 Oct 2007 00:00
Last Modified: 29 Feb 2012 13:24

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