Paper Info
Title
Mining Interesting Rules in Meningitis Data by Cooperatively Using GDT-RS and RSBR
Abstract
This paper describes an application of two rough sets based systems, namely GDT-RS and RSBR respectively, for mining if-then rules in a meningitis dataset. GDT-RS (Generalized Distribution Table and Rough Set) is a soft hybrid induction system, and RSBR (Rough Sets with Boolean Reasoning) is used for discretization of real valued attributes as a preprocessing step realized before the GDT-RS starts. We argue that discretization of continuous valued attributes is an important pre-processing step in the rule discovery process. We illustrate the quality of rules discovered by GDT-RS is strongly affected by the result of discretization.
Year
DOI
Venue
2002
10.1007/3-540-47887-6_40
PAKDD
Keywords
Field
DocType
soft hybrid induction system,rule discovery process,rough sets,rough set,important pre-processing step,mining if-then rule,generalized distribution,meningitis dataset,meningitis data,preprocessing step,boolean reasoning,mining interesting rules,data mining,hybrid system
Data mining,Discretization,Computer science,Rough set,Preprocessor,Information extraction,Boolean algebra,Business process discovery,Hybrid system,Generalized normal distribution
Conference
ISBN
Citations 
PageRank 
3-540-43704-5
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Ning Zhong12907300.63
Juzhen Dong221417.05