NEW APPROACHES FOR DISCOVERING EXCEPTION AND ANOMALOUS RULES

Abstract

Mining association rules is a well known framework for extracting useful knowledge from databases. Despite their proven applicability there exist other approaches that also search for novel and useful information such us peculiarities, infrequent rules, exceptions or anomalous rules. The common feature of these proposals is the low support of such type of rules. So there is a necessity of finding efficient algorithms for extracting them. The principal objective of this paper is providing a unified framework for dealing with such kind of rules. In our case, we take advantage of an existing logic approach called GUHA. This model was first presented in the middle sixties by Hájek et al. and then has been developed by Rauch and others in the last decade. Following this line, this paper also offers some interesting issues. First, it provides a deep analysis of semantics and formulation of exception and anomalous rules. Second, we define the so called double rules as a new type of rules which in conjunction with exceptions and anomalies will describe in more detail the relationship between two sets of items. Third, we give new approaches for mining them and we propose an algorithm with reasonably good performance.