Eecient Search of Reliable Exceptions

Finding patterns from data sets is a fundamental task of data mining. If we categorize all patterns into strong, weak, and random, conventional data mining techniques are designed only to nd strong patterns, which hold for numerous objects and are usually consistent with the expectations of experts. While such strong patterns are helpful in prediction, the unexpectedness and contradiction exhibited by weak patterns are also very useful although they represent a relatively small number of objects. In this paper, we address the problem of nding weak patterns (i.e., reliable exceptions) from databases. A simple and eecient approach is proposed which uses deviation analysis to identify interesting exceptions and explore reliable ones. Besides, it is exible in handling both subjective and objective exceptions. We demonstrate the eeectiveness of the proposed approach through a set of real-life data sets, and present interesting ndings.