Title
Spiral multi-aspect hepatitis data mining
Abstract
When therapy using IFN (interferon) medication for chronic hepatitis patients, various conceptual knowledge/rules will benefit for giving a treatment. The paper describes our work on cooperatively using various data mining agents including the GDT-RS inductive learning system for discovering decision rules, the LOI (learning with ordered information) for discovering ordering rules and important features, as well as the POM (peculiarity oriented mining) for finding peculiarity data/rules, in a spiral discovery process with multi-phase such as pre-processing, rule mining, and post-processing, for multi-aspect analysis of the hepatitis data and meta learning. Our methodology and experimental results show that the perspective of medical doctors will be changed from a single type of experimental data analysis towards a holistic view, by using our multi-aspect mining approach.
Year
DOI
Venue
2003
10.1007/11423270_12
Active Mining
Keywords
Field
DocType
spiral multi-aspect hepatitis data,chronic hepatitis patient,various data mining agent,peculiarity oriented mining,multi-aspect mining approach,rule mining,meta learning,hepatitis data,peculiarity data,experimental data analysis,data mining,decision rule
Decision rule,Data mining,Spiral,Decision table,Experimental data,Rule mining,Hepatitis,Engineering,Business process discovery
Conference
Volume
ISSN
ISBN
3430
0302-9743
3-540-26157-5
Citations 
PageRank 
References 
1
0.36
14
Authors
7
Name
Order
Citations
PageRank
Muneaki Ohshima117011.93
Tomohiro Okuno280.97
Yasuo Fujita310.36
Ning Zhong42907300.63
Juzhen Dong521417.05
Hideto Yokoi619817.11
JZ Dong710.36