Data that appear to have different characteristics than the rest of the population are called
outliers. Identifying outliers from huge data repositories is a very complex task called outlier …

This paper presents an efficient method for mining both positive and negative association
rules in databases. The method extends traditional associations to include association rules …

This paper presents a method for mining exception rules based on a novel measure which
estimates interestingness relative to its corresponding common sense rule and reference …

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 …

Many organizations have an urgent need of mining their multiple databases inherently
distributed in branches (distributed data). In particular, as the Web is rapidly becoming an …

This paper presents an algorithm for discovering surprising exception rules from data sets.
An exception rule, which is defined as a deviational pattern to a common sense, exhibits …

This paper presents an efficient algorithm for discovering exception rules from a data set
without domain-specific information. An exception rule, which is defined as a deviational …

This article presents an algorithm that seeks every possible exception rule that violates a
commonsense rule and satisfies several assumptions of simplicity. Exception rules, which …

Since the introduction of association rules, many algorithms have been developed to
perform the computationally very intensive task of association rule mining. During recent …

A drawback of traditional data-mining methods is that they do not leverage prior knowledge
of users. In prior work, we proposed a method that could discover unexpected patterns in …