A function that finds the local outlier factor (Breunig et al.,2000) of the matrix "data" using k neighbours. The local outlier factor (LOF) is a measure of outlierness that is calculated for each observation. The user decides whether or not an observation will be considered an outlier based on this measure. The LOF takes into consideration the density of the neighbourhood around the observation to determine its outlierness. This is a faster implementation of LOF by using a different data structure and distance calculation function compared to lofactor() function available in dprep package. It also supports multiple k values to be calculated in parallel, as well as various distance measures besides the default Euclidean distance.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
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