Fast anomaly detection despite the duplicates

Authors:
Jay Yoon Lee;U. Kang;Danai Koutra;Christos Faloutsos
Affiliations:
Carnegie Mellon University, Pittsburgh, PA, USA;Korea Advanced Institute of Technology, Daejeon, South Korea;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA
Venue:
Proceedings of the 22nd international conference on World Wide Web companion
Year:
2013

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Abstract

Given a large cloud of multi-dimensional points, and an off-the shelf outlier detection method, why does it take a week to finish? After careful analysis, we discovered that duplicate points create subtle issues, that the literature has ignored: if dmax is the multiplicity of the most over-plotted point, typical algorithms are quadratic on dmax. We propose several ways to eliminate the problem; we report wall-clock times and our time savings; and we show that our methods give either exact results, or highly accurate approximate ones.