LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Angle-based outlier detection in high-dimensional data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
DUSC: Dimensionality Unbiased Subspace Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on 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
OutRank: ranking outliers in high dimensional data
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
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
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
Data Mining and Knowledge Discovery
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Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. However, in recent applications traditional outlier mining approaches miss outliers as they are hidden in subspace projections. In this work, we propose a novel outlier ranking based on the degree of deviation in subspaces. Object deviation is measured only in a selection of relevant subspaces and is based on adaptive neighborhoods in these subspaces. We show that our approach outperforms competing outlier ranking approaches by detecting outliers in arbitrary subspaces.