Data Mining and Knowledge Discovery
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
Learning Rules for Anomaly Detection of Hostile Network Traffic
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Detecting anomalous records in categorical datasets
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Detecting outlying properties of exceptional objects
ACM Transactions on Database Systems (TODS)
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A learning system and its psychological implications
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
An outlier-based data association method for linking criminal incidents
Decision Support Systems - Special issue: Intelligence and security informatics
Conditional Anomaly Detection with Soft Harmonic Functions
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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A wide range of methods have been proposed for detecting different types of outliers in full space and subspaces. However, the interpretability of outliers, that is, explaining in what ways and to what extent an object is an outlier, remains a critical open issue. In this paper, we develop a notion of contextual outliers on categorical data. Intuitively, a contextual outlier is a small group of objects that share strong similarity with a significantly larger reference group of objects on some attributes, but deviate dramatically on some other attributes. We develop a detection algorithm, and conduct experiments to evaluate our approach.