Paper Info
Title
Mining Diabetes Database With Decision Trees and Association Rules
Abstract
Searching for new rules and new knowledge in problem areas, where very little or almost none previous knowledge is present, can be a very long and demanding process. In our research we addressed the problem of finding new knowledge in the form of rules in the diabetes database using a combination of decision trees and association rules. The first question we wanted to answer was, if there are significant differences in sets of rules both approaches produce, and how rules, produced by decision trees behave, after being a subject of filtering and reduction, normally used in association rule approaches. In order to accomplish that, we had to make some modifications to both the decision tree approach and association rule approach. From the first results we can conclude, that the sets of rules, built by decision trees are much smaller than the sets created by association rules. We could also establish, that filtering and reduction did not effect the rules derived from decision trees in the same scale as association rules.
Year
DOI
Venue
2002
10.1109/CBMS.2002.1011367
Computer-Based Medical Systems, 2002.
Keywords
Field
DocType
new knowledge,problem area,mining diabetes database,association rule,association rule approach,fin ing new knowledge,new rule,association rules,decision tree approach,decision trees,decision tree,almostnone previous knowledge,associationrule approach,electrical engineering,diabetes,filtering,data mining,computer science,set theory,databases
Decision rule,Data mining,Decision tree,Set theory,Computer science,Filter (signal processing),Association rule learning,Artificial intelligence,Database,Machine learning
Conference
ISSN
ISBN
Citations 
1063-7125
0-7695-1614-9
3
PageRank 
References 
Authors
0.45
1
5
Name
Order
Citations
PageRank
Milan Zorman15713.07
Gou Masuda2112.51
Peter Kokol330974.52
Ryuichi Yamamoto441.53
Bruno Stiglic5184.92