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Issue No.05  May (2013 vol.25)
pp: 11751180
Ludmila I. Kuncheva , University of Bangor, Bangor
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.226
ABSTRACT
Change detection in streaming data relies on a fast estimation of the probability that the data in two consecutive windows come from different distributions. Choosing the criterion is one of the multitude of questions that need to be addressed when designing a change detection procedure. This paper gives a loglikelihood justification for two wellknown criteria for detecting change in streaming multidimensional data: KullbackLeibler (KL) distance and Hotelling's Tsquare test for equal means (H). We propose a semiparametric loglikelihood criterion (SPLL) for change detection. Compared to the existing loglikelihood change detectors, SPLL trades some theoretical rigor for computation simplicity. We examine SPLL together with KL and H on detecting induced change on 30 real data sets. The criteria were compared using the area under the respective Receiver Operating Characteristic (ROC) curve (AUC). SPLL was found to be on the par with H and better than KL for the nonnormalized data, and better than both on the normalized data.
INDEX TERMS
Detectors, Approximation methods, Covariance matrix, Kernel, Upper bound, Arrays, Monte Carlo methods, loglikelihood detector, Change detection, multidimensional data streams, Hotelling's Tsquare
CITATION
Ludmila I. Kuncheva, "Change Detection in Streaming Multivariate Data Using Likelihood Detectors", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 5, pp. 11751180, May 2013, doi:10.1109/TKDE.2011.226
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