A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Lecture Notes in Computer Science
Local density-based outlier (LOF) is a useful method to detect outliers because of its model free and locally based property. However, the method is very slow for high dimensional datasets. In this paper, we introduce a randomization method that can computer LOF very efficiently for high dimensional datasets. Based on a consistency property of outliers, random points are selected to partition a data set to compute outlier candidates locally. Since the probability of a point to be isolated fromdoi:10.1007/978-3-642-15105-7_17 fatcat:zakor2bxgzgytginlzkgp7lgiq