Using the Triangle Inequality to Accelerate Density based Outlier Detection Method

Discovering outliers in a collection of patterns is a very well known problem that has been studied in various application domains. Density based technique is a popular one for finding outliers in a dataset. This technique calculates outlierness of each pattern using statistics of neighborhood of the pattern. However, density based approaches do not work well with large datasets as these approaches need to compute a large number of distance computations inorder to find neighborhood statistics. In this paper, we propose to utilize triangle inequality based indexing approach to speed up the classical density based outlier detection method LOF. Proposed approach computes less number of distance computations compared to the LOF method. Experimental results demonstrate that our proposed method reduces a significant number of distance computations compared to the LOF method.