Detecting anomalies in cross-classified streams: a Bayesian approach

Deepak Agarwal

doi:10.1007/s10115-006-0036-4

journal article Oct 03, 2006

Detecting anomalies in cross-classified streams: a Bayesian approach

Deepak Agarwal

Knowledge and Information Systems Vol. 11 No. 1 pp. 29-44 · Springer Science and Business Media LLC

View at Publisher Save 10.1007/s10115-006-0036-4

Topics

No keywords indexed for this article. Browse by subject →

References

18

[1]

Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. PODS, Madison, WI 10.1145/543613.543615

[2]

Box GE (1970) Time series analysis: forecasting and control. Holden-Day, London

[3]

Carlin BP, Louis TA (2000) Bayes and empirical Bayes methods for data analysis, 2nd edn. Chapman and Hall/CRC Press, London 10.1201/9781420057669

[4]

Duncan DB (1965) A Bayesian approach to multiple comparisons. Technometrics 7:171–222 10.1080/00401706.1965.10490249

[5]

Kifer D, Ben-David S, Gehrke J (2004) Detecting change in data streams. In: Proceedings of the 30th VLDB conference, Toronto, Canada, pp 180–191 10.1016/b978-012088469-8.50019-x

[6]

Douglas S, Agarwal D, Alonso T, Bell R, Rahim M, Swayne DF, Volinsky C (2004) Mining customer care dialogs for “Daily News”. In : Proceedings of the INTERSPEECH-2004, Jeju, Korea

[7]

DuMouchel W (1988) A Bayesian model and graphical elicitation procedure for multiple comparisons. In: Bernardo JM, DeGroot MH, Lindley DV, Smith AFM (eds) Bayesian statistics 3. Oxford University Press, Oxford.

[8]

Genovese C, Wasserman L (2003) Bayesian and frequentist multiple testing. In: Bayesian statistics 7, Proceedings of the 7th Valencia International Meeting, Tenerife, Spain, pp 145–162. 10.1093/oso/9780198526155.003.0008

[9]

Good P (2000) Permutation tests—a practical guide to resampling methods for testing hypotheses, 2nd edn. Springer, Berlin Heidelberg New York

[10]

Iman RL, Conover W (1987) A measure of top-down correlation. Technometrics 29(3):351–358

[11]

Shaffer JP (1999) A semi-Bayesian study of Duncan's Bayesian multiple comparison procedure. J Stat Plan Inference 82:197–213 10.1016/s0378-3758(99)00042-7

[12]

Gopalan R, Berry DA (1998) Bayesian multiple comparisons using dirichlet process priors. J Am Stat Assoc 93:1130–1139 10.1080/01621459.1998.10473774

[13]

Scott J, Berger J (2003) An exploration of aspects of Bayesian multiple testing. Technical report, Institute of Statistics and Decision Science

[14]

Ganti V, Gehrke JE, Ramakrishnan R (2002) Mining data streams under block evolution. Sigkdd Explorat 3:1–10 10.1145/507515.507517

[15]

DuMouchel W, Volinsky C, Johnson T, Cortes C, Pregibon D (1999) Squashing flat files flatter. In: Proceedings of the 5th ACM SIGKDD Conference, San Diego, CA, pp 6–15 10.1145/312129.312184

[16]

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Yoav Benjamini, Yosef Hochberg

Journal of the Royal Statistical Society Series B:... 1995 10.1111/j.2517-6161.1995.tb02031.x

[17]

Yi BK, Sidiropoulos N, Johnson T, Jagadish HV, Faloutsos C, Biliris A (2000) Online data mining for co-evolving time sequences. In: Proceedings of the 16th International Conference on Data Engineering, San Diego, CA, pp 13–22 10.1109/icde.2000.839383

[18]

Zhu Y, Shasha D (2002) Statstream: statistical monitoring of thousands of data streams in real time. In: Proceedings of the 28th VLDB conference, HongKong, China, pp 358–369

Metrics

51

Citations

18

References

Details

Published: Oct 03, 2006
Vol/Issue: 11(1)
Pages: 29-44
License: View

Authors

D

Deepak Agarwal

Cite This Article

Deepak Agarwal (2006). Detecting anomalies in cross-classified streams: a Bayesian approach. Knowledge and Information Systems, 11(1), 29-44. https://doi.org/10.1007/s10115-006-0036-4

Detecting anomalies in cross-classified streams: a Bayesian approach

You May Also Like