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2007
ACM

Statistical change detection for multi-dimensional data

10 years 11 months ago
Statistical change detection for multi-dimensional data
This paper deals with detecting change of distribution in multi-dimensional data sets. For a given baseline data set and a set of newly observed data points, we define a statistical test called the density test for deciding if the observed data points are sampled from the underlying distribution that produced the baseline data set. We define a test statistic that is strictly distribution-free under the null hypothesis. Our experimental results show that the density test has substantially more power than the two existing methods for multi-dimensional change detection. Categories and Subject Descriptors G.3 [Mathematics of Computing]: PROBABILITY AND STATISTICS--reliability; H.2.8 [DATABASE MANAGEMENT]: Database Applications--algorithms General Terms Reliability, Algorithms Keywords Change detection, density test, kernel density estimation
Xiuyao Song, Mingxi Wu, Christopher M. Jermaine, S
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Xiuyao Song, Mingxi Wu, Christopher M. Jermaine, Sanjay Ranka
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