NDLI: Mining top-n local outliers in large databases

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Mining top-n local outliers in large databases

Content Provider	ACM Digital Library
Author	Jin, Wen Tung, Anthony K. H. Han, Jiawei
Abstract	Outlier detection is an important task in data mining with numerous applications, including credit card fraud detection, video surveillance, etc. A recent work on outlier detection has introduced a novel notion of local outlier in which the degree to which an object is outlying is dependent on the density of its local neighborhood, and each object can be assigned a Local Outlier Factor (LOF) which represents the likelihood of that object being an outlier. Although the concept of local outliers is a useful one, the computation of LOF values for every data objects requires a large number of &kgr;-nearest neighbors searches and can be computationally expensive. Since most objects are usually not outliers, it is useful to provide users with the option of finding only n most outstanding local outliers, i.e., the top-n data objects which are most likely to be local outliers according to their LOFs. However, if the pruning is not done carefully, finding top-n outliers could result in the same amount of computation as finding LOF for all objects. In this paper, we propose a novel method to efficiently find the top-n local outliers in large databases. The concept of "micro-cluster" is introduced to compress the data. An efficient micro-cluster-based local outlier mining algorithm is designed based on this concept. As our algorithm can be adversely affected by the overlapping in the micro-clusters, we proposed a meaningful cut-plane solution for overlapping data. The formal analysis and experiments show that this method can achieve good performance in finding the most outstanding local outliers.
Starting Page	293
Ending Page	298
Page Count	6
File Format	PDF
ISBN	158113391X
DOI	10.1145/502512.502554
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2001-08-26
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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