Robustness of change detection algorithms

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

Volumn 7014 LNCS, Issue , 2011, Pages 125-137

  Dasu, Tamraparni ^a   Krishnan, Shankar ^a   Pomann, Gina Maria ^b  

^a AT AND T LABS RESEARCH   (United States)

^b NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; CHANGE DETECTION ALGORITHMS; COMPUTATIONAL PERFORMANCE; DATA SETS; DATA STREAM; OBJECTIVE PERFORMANCE MEASURES; REAL-WORLD APPLICATION; STATISTICAL PROPERTIES; STREAM MINING; STREAMING DATA; SYNTHETIC DATA;

DATA COMMUNICATION SYSTEMS; DATA REDUCTION; SIGNAL DETECTION;

ALGORITHMS;

EID: 80455127229     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24800-9_14     Document Type: Conference Paper

References (14)

1. Aggarwal, C.C.: A framework for diagnosing changes in evolving data streams. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 575-586 (2003)
2. Chakrabarti, S., Sarawagi, S., Dom, B.: Mining surprising patterns using temporal description length. In: Proceedings of 24rd International Conference on Very Large Databases, pp. 606-617 (1998)
3. Chawathe, S.S., Garcia-Molina, H.: Meaningful change detection in structured data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 26-37 (1997)
4. Cox, D.R., Hinkley, D.V.: Theoretical Statistics. Wiley, New York (1974)
5. Dasu, T., Krishnan, S., Lin, D., Venkatasubramanian, S., Yi, K.: Change (Detection) you can believe in: Finding distributional shifts in data streams. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 21-34. Springer, Heidelberg (2009)
6. Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman and Hall (1993)
7. Ganti, V., Gehrke, J., Ramakrishnan, R., Loh, W.-Y.: A framework for measuring differences in data characteristics, pp. 126-137 (1999)
8. Huber, P.J.: Robust Statistics. John Wiley, New York (1981)
9. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: KDD, pp. 97-106 (2001)
10. Keogh, E., Lonardi, S., Chiu, B.Y.: Finding surprising patterns in a time series database in linear time and space. In: KDD, pp. 550-556 (2002)
11. Kifer, D., Ben-David, S., Gehrke, J.: Detecting changes in data streams. In: Proceedings of the 30th International Conference on Very Large Databases, pp. 180-191 (2004)
12. Kleinberg, J.: Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery 7(4), 373-397 (2003)
13. Song, X., Wu, M., Jermaine, C., Ranka, S.: Statistical change detection for multi-dimensional data. In: ACM SIGKDD 2007, pp. 667-676 (2007)
14. Zhu, Y., Shasha, D.: Efficient elastic burst detection in data streams. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 336-345 (2003)