期刊
SIGMOD RECORD
卷 29, 期 2, 页码 93-104出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/335191.335388
关键词
outlier detection; database mining
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 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-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical.
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