Mining exceptions in databases

Abstract

This paper addresses the problem of mining exceptions from multidimensional databases. The goal of our proposed model is to find association rules that become weaker in some specific subsets of the database. The candidates for exceptions are generated combining previously discovered multidimensional association rules with a set of significant attributes specified by the user. The exceptions are mined only if the candidates do not achieve an expected support. We describe a method to estimate these expectations and propose an algorithm that finds exceptions. Experimental results are also presented. © Springer-Verlag Berlin Heidelberg 2004.