A Note on Accelerating the Local Outlier Factor Method on One-Dimensional Data

Changmuk Kang
2020 ICIC Express Letters  
The local outlier factor (LOF) method, which is proposed by Breunig et al. (2000) , is one of the most common techniques to detect outliers or abnormal data points in a dataset. It compares the density of a data point with the densities of its k-nearest neighbors. This study presents an algorithm to perform LOF much faster than conventional methods, especially for one-dimensional data. Its worst-case time complexity is only O(nk), and space complexity is O(n). The performance is also
more » ... ally compared with the DMwR package, which implements Breunig et al. (2000) in R language.
doi:10.24507/icicel.14.06.571 fatcat:x54iummwvzcptej63j6hlq5eaa