Robust regression and outlier detection
Robust regression and outlier detection
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Outlier identification in high dimensions
Computational Statistics & Data Analysis
Fast mining of distance-based outliers in high-dimensional datasets
Data Mining and Knowledge Discovery
Angle-based outlier detection in high-dimensional data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining influential attributes that capture class and group contrast behaviour
Proceedings of the 17th ACM conference on Information and knowledge management
Detecting outlying properties of exceptional objects
ACM Transactions on Database Systems (TODS)
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
Locality Sensitive Outlier Detection: A ranking driven approach
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Statistical selection of relevant subspace projections for outlier ranking
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Outlier Analysis
Outlier Detection in Arbitrarily Oriented Subspaces
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Outlier ensembles: position paper
ACM SIGKDD Explorations Newsletter
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There exists a variety of traditional outlier models, which measure the deviation of outliers with respect to the full attribute space. However, these techniques fail to detect outliers that deviate only w.r.t. an attribute subset. To address this problem, recent techniques focus on a selection of subspaces that allow: (1) A clear distinction between clustered objects and outliers; (2) a description of outlier reasons by the selected subspaces. However, depending on the outlier model used, different objects in different subspaces have the highest deviation. It is an open research issue to make subspace selection adaptive to the outlier score of each object and flexible w.r.t. the use of different outlier models. In this work we propose such a flexible and adaptive subspace selection scheme. Our generic processing allows instantiations with different outlier models. We utilize the differences of outlier scores in random subspaces to perform a combinatorial refinement of relevant subspaces. Our refinement allows an individual selection of subspaces for each outlier, which is tailored to the underlying outlier model. In the experiments we show the flexibility of our subspace search w.r.t. various outlier models such as distance-based, angle-based, and local-density-based outlier detection.