A New Study of Two Divergence Metrics for Change Detection in Data Streams

Abdulhakim Ali Ali Qahtan, Suojin Wang, Raymond Carroll, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Streaming data are dynamic in nature with frequent changes. To detect such changes, most methods measure the difference between the data distributions in a current time window and a reference window. Divergence metrics and density estimation are required to measure the difference between the data distributions. Our study shows that the Kullback-Leibler (KL) divergence, the most popular metric for comparing distributions, fails to detect certain changes due to its asymmetric property and its dependence on the variance of the data. We thus consider two metrics for detecting changes in univariate data streams: a symmetric KL-divergence and a divergence metric measuring the intersection area of two distributions. The experimental results show that these two metrics lead to more accurate results in change detection than baseline methods such as Change Finder and using conventional KL-divergence.
Original languageEnglish (US)
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Number of pages2
ISBN (Print)9781614994183
StatePublished - Aug 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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