Ten lectures on wavelets
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
ADMIT: anomaly-based data mining for intrusions
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An approach to spacecraft anomaly detection problem using kernel feature space
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A nonparametric outlier detection for effectively discovering top-n outliers from engineering data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Data Mining for Intrusion Detection: From Outliers to True Intrusions
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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Atypical behaviours are the basis of a valuable knowledge in domains related to security (e.g. fraud detection for credit card [1], cyber security [4] or safety of critical systems [6]). Atypicity generally depends on the isolation level of a (set of) records, compared to the dataset. One possible method for finding atypic records aims to perform two steps. The first step is a clustering (grouping the records by similarity) and the second step is the identification of clusters that do not correspond to a satisfying number of records. The main problem is to adjust the method and find the good level of atypicity. This issue is even more important in the domain of data streams, where a decision has to be taken in a very short time and the end-user does not want to try several settings. In this paper, we propose Mrab , a self-adjusting approach intending to automatically discover atypical behaviours (in the results of a clustering algorithm) without any parameter. We provide the formal framework of our method and our proposal is tested through a set of experiments.