Paper Details: Downloads: 1220
Serial Number: P1121602462
Title: Enhancing Grid Local Outlier Factor Algorithm for better Outlier Detection
Authors: Eslam Mahmoud and Ahmed M. Elmogy and Amany Sarhan
Abstract: Detecting outliers in a large data set is a major data mining task. The existing approaches in this field are categorized into two main categories which are distance-based and density-based outlier detection approaches. Although, Local Outlier Factor (LOF) is considered as the most popular density-based algorithm, it still has some problems related to the speed and accuracy. Enhancing LOF algorithm has been the focus of many researchers working in this field. Among the improved versions of LOF, GridLOF has been proven to have a very good performance. This paper presents an enhancement to GridLOF algorithm by replacing one of its steps by a less complex step which reduces the complexity to be only O(N) instead of O(N^2 ) in a novel way. The simulation results show that the proposed algorithm outperforms GridLOF algorithm in terms of speed and accuracy.
Keywords: Outlier, outlier detection, data mining, LOF, GridLOF
Journal/Conference: International Journal of Artificial Intelligence and Machine Learning
Volume: 16
Issue: 1
Submission Date: 1/4/2016 12:00:00 AM
Review Date: 2/22/2016 12:00:00 AM
Publishing Date: 3/23/2016 12:00:00 AM
Article Downloads: 1220
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