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A Note on Accelerating the Local Outlier Factor Method on One-Dimensional Data
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 alsodoi:10.24507/icicel.14.06.571 fatcat:x54iummwvzcptej63j6hlq5eaa