2015 APCABasedChangeDetectionFramewo

• (Qahtan et al., 2015) ⇒ Abdulhakim A. Qahtan, Basma Alharbi, Suojin Wang, and Xiangliang Zhang. (2015). “A PCA-Based Change Detection Framework for Multidimensional Data Streams: Change Detection in Multidimensional Data Streams.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783359

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+A+PCA-Based+Change+Detection+Framework+for+Multidimensional+Data+Streams%3A+Change+Detection+in+Multidimensional+Data+Streams
• http://dl.acm.org/citation.cfm?id=2783258.2783359&preflayout=flat#citedby

Quotes

Author Keywords

• Change detection; data mining; data streams; density estimation; principal component analysis

Abstract

Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations.

The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 APCABasedChangeDetectionFramewo	Xiangliang Zhang Abdulhakim A. Qahtan Basma Alharbi Suojin Wang			A PCA-Based Change Detection Framework for Multidimensional Data Streams: Change Detection in Multidimensional Data Streams				10.1145/2783258.2783359		2015