LOF: Identifying Density-Based Local Outliers

@inproceedings{Breunig2000LOFID,
  title={LOF: Identifying Density-Based Local Outliers},
  author={Markus M. Breunig and Hans-Peter Kriegel and Raymond T. Ng and J{\"o}rg Sander},
  booktitle={SIGMOD Conference},
  year={2000}
}
For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends… CONTINUE READING
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