LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd 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
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
Outlier Mining in Large High-Dimensional Data Sets
IEEE Transactions on Knowledge and Data Engineering
An effective and efficient algorithm for high-dimensional outlier detection
The VLDB Journal — The International Journal on Very Large Data Bases
Mining distance-based outliers from large databases in any metric space
Proceedings of the 12th 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
Projected outlier detection in high-dimensional mixed-attributes data set
Expert Systems with Applications: An International Journal
OutRank: ranking outliers in high dimensional data
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
Communications of the ACM
Mining outliers with ensemble of heterogeneous detectors on random subspaces
DASFAA'10 Proceedings of the 15th 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
AUDIO: an integrity auditing framework of outlier-mining-as-a-service systems
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Fast top-k distance-based outlier detection on uncertain data
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Data Mining and Knowledge Discovery
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Traditional outlier detection techniques usually fail to work efficiently on high-dimensional data due to the curse of dimensionality. This work proposes a novel method for subspace outlier detection, that specifically deals with multidimensional spaces where feature relevance is a local rather than a global property. Different from existing approaches, it is not grid-based and dimensionality unbiased. Thus, its performance is impervious to grid resolution as well as the curse of dimensionality. In addition, our approach ranks the outliers, allowing users to select the number of desired outliers, thus mitigating the issue of high false alarm rate. Extensive empirical studies on real datasets show that our approach efficiently and effectively detects outliers, even in highdimensional spaces.