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Published in:
2009 | OriginalPaper | Chapter
Extracting Decision Correlation Rules
Authors : Alain Casali, Christian Ernst
Published in: Database and Expert Systems Applications
Publisher: Springer Berlin Heidelberg
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In this paper, two concepts are introduced: decision correlation rules and contingency vectors. The first concept results from a cross fertilization between correlation and decision rules. It enables relevant links to be highlighted between sets of patterns of a binary relation and the values of target items belonging to the same relation on the twofold basis of the Chi-Squared measure and of the support of the extracted patterns. Due to the very nature of the problem, levelwise algorithms only allow extraction of results with long execution times and huge memory occupation. To offset these two problems, we propose an algorithm based both on the lectic order and contingency vectors, an alternate representation of contingency tables.