Author/Editor		Zorman, Milan; Masuda, Gou; Kokol, Peter; Yamamoto, Ryuichi; Stiglic, Bruno
Title		Mining diabetes database with decision trees and association rules
Type		članek
Source		In: Kokol P, Stiglic B, Zorman M, et al, editors. Proceedings of the 15th IEEE symposium on computer-based medical systems (CBMS 2002); 2002 Jun 4-7; Maribor. Los Alamitos: Institute of electrical and electronics engineers,
Publication year		2002
Volume		str. 134-9
Language		eng
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. /n 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.
Descriptors		DIABETES MELLITUS DECISION TREES