HiCS: High Contrast Subspaces for Density-Based Outlier Ranking

—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 enhan...

• Type: Journal
• Year: 2012
• Where: ICDE
• Authors: Fabian Keller, Emmanuel Müller, Klemens Böhm
• Keywords: Conditional Dependence, Database, Dimensionality Reduction, ICDE 2012, Random Listings