Local subspace-based outlier detection using global neighbourhoods

  title={Local subspace-based outlier detection using global neighbourhoods},
  author={Bas van Stein and Matthijs van Leeuwen and Thomas B{\"a}ck},
  journal={2016 IEEE International Conference on Big Data (Big Data)},
Outlier detection in high-dimensional data is a challenging yet important task, as it has applications in, e.g., fraud detection and quality control. State-of-the-art density-based algorithms perform well because they 1) take the local neighbourhoods of data points into account and 2) consider feature subspaces. In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often… 

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