Article ID Journal Published Year Pages File Type
4633136 Applied Mathematics and Computation 2008 16 Pages PDF
Abstract

Previously, exception rules have been defined as association rules with low support and high confidence. Exception rules are important in data mining, as they form rules that can be categorized as an exception. This is the opposite of general association rules in data mining, which focus on high support and high confidence. In this paper, a new approach to mining exception rules is proposed and evaluated. A relationship between exception and positive/negative association rules is considered, whereby the candidate exception rules are generated based on knowledge of the positive and negative association rules in the database. As a result, the exception rules exist in the form of negative, as well as positive, association. A novel exceptionality measure is proposed to evaluate the candidate exception rules. The candidate exceptions with high exceptionality form the final set of exception rules. Algorithms for mining exception rules are developed and evaluated using an exceptionality measurement, the desired performance of which has been proven.

Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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