LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference 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
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring the interestingness of articles in a limited user environment
Information Processing and Management: an International Journal
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In this paper we present an unsupervised distance-based outlier detection method designed to learn a model over the objects contained in a data set. The learned model, called solving set, is a small subset of the data set that is used to classify new unseen objects as outliers or not. We provide an algorithm that computes a solving set with sub-quadratic time requirements, and we give experimental evidence that the computed solving set is small and that the false positive rate, i.e. the fraction of new objects misclassified as outliers using the solving set instead of the overall data set, is negligible.