Please use this identifier to cite or link to this item:
Title: Rule mining with prior knowledge-a belief networks approach
Authors: Zhou, Z.
Liu, H. 
Li, S.Z.
Chua, C.S.
Keywords: belief networks
classification rule
correlation rule
machine learning
rule mining
Issue Date: 2001
Source: Zhou, Z.,Liu, H.,Li, S.Z.,Chua, C.S. (2001). Rule mining with prior knowledge-a belief networks approach. Intelligent Data Analysis 5 (2) : 95-110. ScholarBank@NUS Repository.
Abstract: Some existing data mining methods, such as classification trees, neural networks and association rules, have the drawbacks that the user's prior knowledge cannot be easily specified and incorporated into the knowledge discovery process, and the rules mined from databases lack quantitative analyses. In this paper, we propose a belief networks method for rule mining, which takes the advantage of belief networks as the directed acyclic graph language and their function for numerical representation of probabilistic dependencies among the variables in the database, so that it can overcome the drawbacks. Since belief networks provide a natural representation for capturing causal relationship among a set of variables, our proposed method can mine more general correlation rules which can capture the relationship of more than two attribute variables. The potential application of the proposed method is demonstrated through the detailed case studies on benchmark databases. © 2001-IOS Press.
Source Title: Intelligent Data Analysis
ISSN: 1088467X
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Mar 10, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.