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

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Xiuyao Song, Mingxi Wu, Christopher M. Jermaine, Sanjay Ranka
• Keywords: Baseline Data Set, Data Mining, Density Test, KDD 2007, Observed Data Points