Abstract
We propose an interestingness measure for groups of classification rules which are mutually related based on the Minimum Description Length Principle. Unlike conventional methods, our interestingness measure is based on a theoretical background, has no parameter, is applicable to a group of any number of rules, and can exploit an initial hypothesis. We have integrated the interestingness measure with practical heuristic search and built a rule-group discovery method CLARDEM (Classification Rule Discovery method based on an Extended-Mdlp). Extensive experiments using both real and artificial data confirm that CLARDEM can discover the correct concept from a small noisy data set and an approximate initial concept with high "discovery accuracy".
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