A General Framework for Mining Massive Data Streams

Journal of Computational and Graphical Statistics

Volumn 12, Issue 4, 2003, Pages 945-949

  Domingos, Pedro ^a   Hulten, Geoff ^a  

^a University of Washington   (United States)

Author keywords

Data mining; Hoeffding bounds; Machine learning; Scalability; Subsampling

Indexed keywords

EID: 0742323983     PISSN: 10618600     EISSN: None     Source Type: Journal    
DOI: 10.1198/1061860032544     Document Type: Conference Paper

References (6)

1. Domingos, P., and Hulten, G. (2000), "Mining High-Speed Data Streams," in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA: ACM Press, pp. 71-80.
2. _ (2001), "A General Method for Scaling up Machine Learning Algorithms and its Application to Clustering," in Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA: Morgan Kaufmann, pp. 106-113.
3. _ (2002), "Learning from Infinite Data in Finite Time," in Advances in Neural Information Processing Systems 14, eds. T. G. Dietterich, S. Becker, and Z. Ghahramani, Cambridge, MA: MIT Press, pp. 673-680.
4. Hoeffding, W. (1963), "Probability Inequalities for Sums of Bounded Random Variables," Journal of the American Statistical Association, 58, 13-30.
5. Hulten, G., and Domingos, P. (2002), "Mining Complex Models from Arbitrarily Large Databases in Constant Time," in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada: pp. 525-531.
6. Hulten, G., Spencer, L., and Domingos, P. (2001), "Mining Time-Changing Data Streams," in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA: ACM Press, pp. 97-106.