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Robustness of change detection algorithms

Publication ,  Conference
Dasu, T; Krishnan, S; Pomann, GM
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
November 9, 2011

Stream mining is a challenging problem that has attracted considerable attention in the last decade. As a result there are numerous algorithms for mining data streams, from summarizing and analyzing, to change and anomaly detection. However, most research focuses on proposing, adapting or improving algorithms and studying their computational performance. For a practitioner of stream mining, there is very little guidance on choosing a technology suited for a particular task or application. In this paper, we address the practical aspect of choosing a suitable algorithm by drawing on the statistical properties of power and robustness. For the purpose of illustration, we focus on change detection algorithms (CDAs). We define an objective performance measure, streaming power, and use it to explore the robustness of three different algorithms. The measure is comparable for disparate algorithms, and provides a common framework for comparing and evaluating change detection algorithms on any data set in a meaningful fashion. We demonstrate on real world applications, and on synthetic data. In addition, we present a repository of data streams for the community to test change detection algorithms for streaming data. © 2011 Springer-Verlag.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783642247996

Publication Date

November 9, 2011

Volume

7014 LNCS

Start / End Page

125 / 137

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Dasu, T., Krishnan, S., & Pomann, G. M. (2011). Robustness of change detection algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7014 LNCS, pp. 125–137). https://doi.org/10.1007/978-3-642-24800-9_14
Dasu, T., S. Krishnan, and G. M. Pomann. “Robustness of change detection algorithms.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7014 LNCS:125–37, 2011. https://doi.org/10.1007/978-3-642-24800-9_14.
Dasu T, Krishnan S, Pomann GM. Robustness of change detection algorithms. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011. p. 125–37.
Dasu, T., et al. “Robustness of change detection algorithms.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7014 LNCS, 2011, pp. 125–37. Scopus, doi:10.1007/978-3-642-24800-9_14.
Dasu T, Krishnan S, Pomann GM. Robustness of change detection algorithms. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011. p. 125–137.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783642247996

Publication Date

November 9, 2011

Volume

7014 LNCS

Start / End Page

125 / 137

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences