HiCS: High Contrast Subspaces for Density-Based Outlier Ranking

@article{Keller2012HiCSHC,
  title={HiCS: High Contrast Subspaces for Density-Based Outlier Ranking},
  author={Fabian Keller and Emmanuel M{\"u}ller and Klemens B{\"o}hm},
  journal={2012 IEEE 28th International Conference on Data Engineering},
  year={2012},
  pages={1037-1048}
}
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… CONTINUE READING

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