ACM Computing Surveys (CSUR)
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Learning video preferences from video content
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
Angle-based outlier detection in high-dimensional data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting outlying properties of exceptional objects
ACM Transactions on Database Systems (TODS)
Detecting Projected Outliers in High-Dimensional Data Streams
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Detecting outliers in categorical record databases based on attribute associations
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Detecting outliers on arbitrary data streams using anytime approaches
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Can shared-neighbor distances defeat the curse of dimensionality?
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Journal of Intelligent Information Systems
International Journal of Computational Science and Engineering
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
A survey on unsupervised outlier detection in high-dimensional numerical data
Statistical Analysis and Data Mining
Anomaly detection in large-scale data stream networks
Data Mining and Knowledge Discovery
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Detecting outliers is an important problem. Most of its applications typically possess high dimensional datasets. In high dimensional space, the data becomes sparse which implies that every object can be regarded as an outlier from the point of view of similarity. Furthermore, a fundamental issue is that the notion of which objects are outliers typically varies between users, problem domains or, even, datasets. In this paper, we present a novel robust solution which detects high dimensional outliers based on user examples and tolerates incorrect inputs. It studies the behavior of projections of such a few examples, to discover further objects that are outstanding in the projection where many examples are outlying. Our experiments on both real and synthetic datasets demonstrate the ability of the proposed method to detect outliers corresponding to the user examples.