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User sheymann
title Outskewer: Using Skewness to Spot Outliers in Samples and Time Series
Authors Sébastien Heymann, Matthieu Latapy and Clémence Magnien
Source Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Year 2012
Type Conference
Type of publication ISC-PIF scholar

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Abstract Finding outliers in datasets is a classical problem of high interest for (dynamic) social network analysis. However, most methods rely on assumptions which are rarely met in practice, such as prior knowledge of some outliers or about normal behavior. We propose here Outskewer, a new approach based on the notion of skewness (a measure of the symmetry of a distribution) and its evolution when extremal values are removed one by one. Our method is easy to set up, it requires no prior knowledge on the system, and it may be used on-line. We illustrate its performance on two data sets representative of many use-cases: evolution of ego-centered views of the internet topology, and logs of queries entered into a search engine.
Link http://www-rp.lip6.fr/~magnien/DynGraph/Docs/2012_asonam.pdf
Created Monday 18 February, 2013 18:54:22


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