Computing depth contours of bivariate point clouds
Computational Statistics & Data Analysis - Special issue on classification
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
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
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
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A unified approach for mining outliers
CASCON '97 Proceedings of the 1997 conference of the Centre for Advanced Studies on Collaborative research
Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
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
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Example-Based Robust Outlier Detection in High Dimensional Datasets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An Efficient Reference-Based Approach to Outlier Detection in Large Datasets
ICDM '06 Proceedings of the Sixth International Conference on 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
Ranking outliers using symmetric neighborhood relationship
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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
A Fast Feature-Based Method to Detect Unusual Patterns in Multidimensional Datasets
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
LoOP: local outlier probabilities
Proceedings of the 18th ACM conference on Information and knowledge management
Detecting outliers on arbitrary data streams using anytime approaches
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Adaptive outlierness for subspace outlier ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Can shared-neighbor distances defeat the curse of dimensionality?
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
A fast randomized method for local density-based outlier detection in high dimensional data
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
SOREX: subspace outlier ranking exploration toolkit
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
A taxi business intelligence system
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual evaluation of outlier detection models
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A minimum spanning tree-inspired clustering-based outlier detection technique
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
A survey on unsupervised outlier detection in high-dimensional numerical data
Statistical Analysis and Data Mining
Subsampling for efficient and effective unsupervised outlier detection ensembles
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Flexible and adaptive subspace search for outlier analysis
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Data Mining and Knowledge Discovery
Exploiting domain knowledge to detect outliers
Data Mining and Knowledge Discovery
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. All existing approaches, however, are based on an assessment of distances (sometimes indirectly by assuming certain distributions) in the full-dimensional Euclidean data space. In high-dimensional data, these approaches are bound to deteriorate due to the notorious "curse of dimensionality". In this paper, we propose a novel approach named ABOD (Angle-Based Outlier Detection) and some variants assessing the variance in the angles between the difference vectors of a point to the other points. This way, the effects of the "curse of dimensionality" are alleviated compared to purely distance-based approaches. A main advantage of our new approach is that our method does not rely on any parameter selection influencing the quality of the achieved ranking. In a thorough experimental evaluation, we compare ABOD to the well-established distance-based method LOF for various artificial and a real world data set and show ABOD to perform especially well on high-dimensional data.