Robust outlier detection using commute time and eigenspace embedding

Authors:
Nguyen Lu Dang Khoa;Sanjay Chawla
Affiliations:
School of Information Technologies, University of Sydney, Sydney, NSW, Australia;School of Information Technologies, University of Sydney, Sydney, NSW, Australia
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2010

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Abstract

We present a method to find outliers using ‘commute distance' computed from a random walk on graph Unlike Euclidean distance, commute distance between two nodes captures both the distance between them and their local neighborhood densities Indeed commute distance is the Euclidean distance in the space spanned by eigenvectors of the graph Laplacian matrix We show by analysis and experiments that using this measure, we can capture both global and local outliers effectively with just a distance based method Moreover, the method can detect outlying clusters which other traditional methods often fail to capture and also shows a high resistance to noise than local outlier detection method Moreover, to avoid the O(n3) direct computation of commute distance, a graph component sampling and an eigenspace approximation combined with pruning technique reduce the time to O(nlogn) while preserving the outlier ranking.