Paper Info
Title
A hybrid approach to rule discovery in databases
Abstract
This paper introduces a hybrid approach for rule discovery in databases in an environment with uncertainty and incompleteness. We first create an appropriate relationship between deductive reasoning and stochastic process, and extend the relationship for including abduction. Then, we define a Generalization Distribution Table (GDT), which is a variant of transition matrix in stochastic process, as a hypothesis search space for generalization, and describe that the GDT can be represented by knowledge-oriented networks. Furthermore, we describe a discovery process based on the network representation. Finally, we introduce some extension for making our approach more useful, and discuss some problems for real applications. We discuss inductive methods from the viewpoint of the value of information, and describe that the main features of our approach are: (1) the uncertainty of a rule, including its ability to predict possible instances, can be explicitly represented in the strength of the rule, (2) noisy data and data change can be handled effectively, (3) biases can be flexibly selected and background knowledge can be used in the discovery process for constraint and search control, and (4) if-then rules can be discovered in an evolutionary, parallel-distributed cooperative mode. (C) 2000 Elsevier Science Inc. All rights reserved.
Year
DOI
Venue
2000
10.1016/S0020-0255(00)00012-8
Inf. Sci.
Keywords
Field
DocType
hybrid approach,stochastic process,search space,value of information,transition matrix
Noisy data,Stochastic matrix,Stochastic process,Deductive reasoning,Value of information,Artificial intelligence,Business process discovery,Machine learning,Database,Mathematics
Journal
Volume
Issue
ISSN
126
1-4
0020-0255
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Ning Zhong12907300.63
Juzhen Dong221417.05
Setsuo Ohsuga3960222.02