Autonomous Discovery of Reliable Exception Rules

This paper presents an autonomous algorithm for discovering exception rules from data sets. An exception rule, which is defined as a deviational pattern to a well-known fact, exhibits unexpectedness and is sometimes extremely useful in spite of its obscurity. Previous discovery approaches for this type of knowledge have neglected the problem of evalnating the reliability of the rules extracted from a data set. It is clear, however, that this question is mandatory in distingnishing knowiedge from nnreliabie patterns witilout annoying the users. In order to circumvent these difficulties we propose a probabilistic estimation approach in which we obtain an exception rule associated with a common sense rule in the form of a rule pair. Onr approach discovers, based on the normal approximations of the multinomial distributions, rule pairs which satisfy, with high confidence, all the specified conditions. The time efficiency of the discovery process is improved by the newly-derived stopping c...

• Type: Conference
• Year: 1997
• Where: KDD
• Authors: Einoshin Suzuki
• Keywords: Data Mining, Data Sets, Exception Rule, KDD 1997, Rule Pairs