Fast discovery of association rules
Advances in knowledge discovery and data mining
Machine learning and data mining
Communications of the ACM
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Discovery of Surprising Exception Rules Based on Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Mining Significant Pairs of Patterns from Graph Structures with Class Labels
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Efficient Pruning Schemes for Distance-Based Outlier Detection
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
“Rule + exception” strategies for knowledge management and discovery
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Undirected exception rule discovery as local pattern detection
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Mining exceptions in databases
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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This paper presents an algorithm that seeks every possible exception rule which violates a common sense rule and satisfies several assumptions of simplicity. Exception rules, which represent systematic deviation from common sense rules, are often found interesting. Discovery of pairs that consist of a common sense rule and an exception rule, resulting from undirected search for unexpected exception rules, was successful in various domains. In the past, however, an exception rule represented a change of conclusion caused by adding an extra condition to the premise of a common sense rule. That approach formalized only one type of exceptions, and failed to represent other types. In order to provide a systematic treatment of exceptions, we categorize exception rules into eleven categories, and we propose a unified algorithm for discovering all of them. Preliminary results on fifteen real-world data sets provide an empirical proof of effectiveness of our algorithm in discovering interesting knowledge. The empirical results also match our theoretical analysis of exceptions, showing that the eleven types can be partitioned in three classes according to the frequency with which they occur in data.