Monitoring incremental histogram distribution for change detection in data streams

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

Volumn 5840 LNCS, Issue , 2010, Pages 25-42

  Sebastião, Raquel ^a   Gama, João ^a   Rodrigues, Pedro Pereira ^a,b   Bernardes, João ^b,c  

^a UNIVERSITY OF PORTO   (Portugal)

^b University of Porto   (Portugal)

^c INEB INSTITUTO DE ENGENHARIA BIOMÉDICA   (Portugal)

Author keywords

Adaptive Cumulative Windows; Change detection; Data streams; Learning histograms; Machine learning; Monitoring data distribution

Indexed keywords

ADAPTIVE CUMULATIVE WINDOWS; CHANGE DETECTION; DATA STREAM; LEARNING HISTOGRAMS; MACHINE-LEARNING;

DATA COMMUNICATION SYSTEMS; DATA REDUCTION; LEARNING SYSTEMS; MONITORING; SENSORS; SIGNAL DETECTION; STATISTICAL METHODS;

GRAPHIC METHODS;

EID: 77957913416     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12519-5_2     Document Type: Conference Paper

References (29)

1. Ayres-de-Campos, D., Sousa, P., Costa, A., Bernardes, J.: Omniview-SisPorto® 3.5-a central fetal monitoring station with online alerts based on computerized cardiotocogram+ST event analysis. J. Perinat. Med. 36, 260-264 (2008)
2. Ayres-de-Campos, D., Bernardes, J., Garrido, A., Marques-de-Śa, J., Pereira-Leite, L.: SisPorto 2.0: a program for automated analysis of cardiotocograms. J. Matern Fetal Med. 9, 311-318 (2000)
3. Barbaŕa, D.: Requirements for clustering data streams. SIGKDD Explorations (Special Issue on Online, Interactive and Anytime Data Mining) 3(2), 23-27 (2002)
4. Berthold, M., Hand, D.: Intelligent Data Analysis-An Introduction. Springer, Heidelberg (1999)
5. Breiman, L., et al.: Classification and Regression Trees. Chapman & Hall, Boca Raton (1993)
6. Bock, R.K., Savicky, P.: MAGIC Gamma Telescope Benchmark (2007), http://archive.ics.uci.edu/ml/datasets.html
7. Chabert, M., Ruiz, D., Tourneret, J.-Y.: Optimal wavelet for abrupt change detec-tion in multiplicative noise. In: IEEE International Conference on Acoustics Speech and Signal Processing, May 2004, pp. 1089-1092 (2004)
8. Cormode, G., Garofalakis, M.: Sketching probabilistic data streams. In: Proceed-ings of the 2007 ACM SIGMOD International Conference on Management of Data, June 2007, pp. 281-292 (2007)
9. Correa, M., de J. Raḿirez, M., Bielza, C., Pamies, J., Alique, J.R.: Prediction of surface quality using probabilistic models. In: 7th Congress of the Colombian Association of Automatic, Cali, Colombia, March 21-24 (2007) (in Spanish)
10. Dasu, T., Krishnan, S., Venkatasubramanian, S., Yi, K.: An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams. In: Interface 2006, Pasadena, CA Report (2006)
11. Gama, J., Medas, P., Castillo, G., Rodrigues, P.P.: Learning with Drift Detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286-295. Springer, Heidelberg (2004)
12. Gama, J., Pinto, C.: Discretization from Data Streams: applications to Histograms and Data Mining. In: Proceedings of the 2006 ACM Symposium on Applied Com-puting, pp. 662-667 (2006)
13. Goņcalves, H., Bernardes, J., Paula Rocha, J.A., et al.: Linear and nonlinear analy-sis of heart rate patterns associated with fetal behavioral states in the antepartum period. Early Human Development 83(9), 585-591 (2007)
14. Guha, S., Koudas, N., Woo, J.: REHIST: Relative error histogram construction algorithms. In: Proceedings of the VLDB Conference, pp. 300-311 (2004)
15. Guha, S., Harb, B.: Wavelet synopsis for data streams: minimizing non-euclidean error. In: Proceedings of the eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, August 2005, pp. 88-97 (2005)
16. Guha, S., Koudas, N., Shim, K.: Approximation and streaming algorithms for histogram construction problems. ACM Transactions on Database Systems (TODS) 31(1), 396-438 (2006)
17. Hulten, G., Spencer, L., Domingos, P.: Mining Time-Changing Data Streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM SIGKDD 2001, pp. 97-106. ACM Press, New York (2001)
18. Jagadish, H.V., Koudas, N., Muthukrishnan, S., Poosala, V., Sevcik, K.C., Suel, T.: Optimal Histograms with Quality Guarantees. In: Proc. of the VLDB Conference, pp. 275-286 (1998)
19. Karras, P., Sacharidis, D., Mamoulis, N.: Exploiting Duality in Summarization with Deterministic Guarantees. Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 380-389 (2007)
20. Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: VLDB 2004: Proceedings of the 30th International Conference on Very Large Data Bases, pp. 180-191. Morgan Kaufmann Publishers Inc., San Francisco (2004)
21. Klinkenberg, R., Renz, I.: Adaptive information filtering: Learning in the presence of concept drifts. In: Learning for Text Categorization, pp. 33-40. AAAI Press, Menlo Park (1998)
22. Klinkenberg, R., Joachims, T.: Detecting concept drift with support vector ma-chines. In: Proceedings of ICML 2000, 17th International Conference on Machine Learning, Stanford, US, pp. 487-494. Morgan Kaufmann Publishers, San Francisco (2000)
23. Klinkenberg, R.: Learning drifting concepts: Example selection vs. example weight-ing. Intelligent Data Analysis 8(3), 281-300 (2004)
24. Maloof, M., Michalski, R.: Selecting examples for partial memory learning. Machine Learning 41, 27-52 (2000)
25. Pestana, D.D., Velosa, S.F.: Introdu̧c̃aòa Probabilidadeèa Estat̀istica. Funda̧c̃ao Calouste Gulbenkian (2002)
26. Pinto, C., Gama, J.: Incremental discretization, application to data with concept drift. In: Proceedings of the 2007 ACM Symposium on Applied Computing, March 2007, pp. 467-468 (2007)
27. Sebastĩao, R., Gama, J.: Change Detection in Learning Histograms from Data Streams. In: Proceedings of Portuguese Conference on Artificial Intelligence, Guimar̃aes, Portugal (December 2007)
28. Spinosa, E.J., Carvalho, A., Gama, J.: OLINDDA: A cluster-based approach for detecting novelty and concept drift in data streams. In: Proceedings of the 2007 ACM Symposium on Applied Computing, March 2007, pp. 448-452 (2007)
29. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69-101 (1996)