Using the Triangle Inequality to Accelerate Density based Outlier Detection Method

  • Patra B
  • 6


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


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.

Author-supplied keywords

  • Large datasets
  • Outlier detection
  • triangle inequality
  • {LOF}

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in


  • Bidyut Kr. Patra

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free