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Statistical Change Detection for Multi-Dimensional Data
This video was recorded at 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007. 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
