LOF: Identifying density-based local outliers

For many KDD applications, such as detecting criminal activities in E-comme rce, finding the rare instances or the outliers, can. be more interesting t han 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 a n object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-wor ld datasets, we demonstrate that LOF can be used to find outliers which app ear to be meaningful, but can otherwise not be identified with existing app roaches. Finally, a careful performance evaluation of our algorithm confirm s we show that our approach of finding local outliers can be practical.