A survey on unsupervised outlier detection in high-dimensional numerical data
Statistical Analysis and Data Mining
OutRules: a framework for outlier descriptions in multiple context spaces
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Interactive data mining with 3D-parallel-coordinate-trees
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Outlier ensembles: position paper
ACM SIGKDD Explorations Newsletter
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
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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Outlier mining is a major task in data analysis. Outliers are objects that highly deviate from regular objects in their local neighborhood. Density-based outlier ranking methods score each object based on its degree of deviation. In many applications, these ranking methods degenerate to random listings due to low contrast between outliers and regular objects. Outliers do not show up in the scattered full space, they are hidden in multiple high contrast subspace projections of the data. Measuring the contrast of such subspaces for outlier rankings is an open research challenge. In this work, we propose a novel subspace search method that selects high contrast subspaces for density-based outlier ranking. It is designed as pre-processing step to outlier ranking algorithms. It searches for high contrast subspaces with a significant amount of conditional dependence among the subspace dimensions. With our approach, we propose a first measure for the contrast of subspaces. Thus, we enhance the quality of traditional outlier rankings by computing outlier scores in high contrast projections only. The evaluation on real and synthetic data shows that our approach outperforms traditional dimensionality reduction techniques, naive random projections as well as state-of-the-art subspace search techniques and provides enhanced quality for outlier ranking.