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
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
An Efficient Reference-Based Approach to Outlier Detection in Large Datasets
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Angle-based outlier detection in high-dimensional data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
LoOP: local outlier probabilities
Proceedings of the 18th ACM conference on Information and knowledge management
Spatial outlier detection: data, algorithms, visualizations
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Stream data mining using the MOA framework
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
A survey on unsupervised outlier detection in high-dimensional numerical data
Statistical Analysis and Data Mining
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Many outlier detection methods do not merely provide the decision for a single data object being or not being an outlier. Instead, many approaches give an “outlier score” or “outlier factor” indicating “how much” the respective data object is an outlier. Such outlier scores differ widely in their range, contrast, and expressiveness between different outlier models. Even for one and the same outlier model, the same score can indicate a different degree of “outlierness” in different data sets or regions of different characteristics in one data set. Here, we demonstrate a visualization tool based on a unification of outlier scores that allows to compare and evaluate outlier scores visually even for high dimensional data.