D. Sujatha, M.Anil Kumar


Data stream is a collection or sequence of data instances of infinite length. Stream classification or online classification is more challenging task due to speed, diversity of concept or nature, type of distribution (linear or skewed), heterogeneous of data sources, lack of re-reading of instances and possibility of recurrence. This paper focuses on the concept drift under recurrence. The major challenge in data stream is handling of high volume of data of infinite length. Classification of instances under concept drift and recurrence is more difficult due to maintenance of past classifier results. To handle this situation in more efficient manner through swapping technique followed operating system’s demand paging concept with little modifications


Data Stream Mining; Concept Drift; Recurrence Concept Drift;


N.C. Oza and S. Russell, “Experimental Comparisons of Online and Batch Versions of Bagging and Boosting,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 359-364, 2001.

A. Fern and R. Givan, “Online Ensemble Learning: An Empirical Study,” Machine Learning, vol. 53, pp. 71-109, 2003.

F.L. Minku, A. White, and X. Yao, “The Impact of Diversity on On-Line Ensemble Learning in the Presence of Concept Drift,” IEEETrans. Knowledge and Data Eng., vol. 22, no. 5, pp. 730-742, http:// dx.doi.org/10.1109/TKDE.2009.156, May 2010.

J.Z. Kolter and M.A. Maloof, “Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts,” J. Machine Learning Research, vol. 8, pp. 2755-2790, 2007.

M. Baena-Garcı´a, J. Del Campo-_ Avila, R. Fidalgo, and A. Bifet, “Early Drift Detection Method,” Proc. Fourth ECML PKDD Int’l Workshop Knowledge Discovery from Data Streams (IWKDDS ’06), pp. 77-86, 2006.

H. Wang, W. Fan, P.S. Yu, and J. Han, “Mining Concept-Drifting Data Streams Using Ensemble Classifiers,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 226-235, 2003.

M. Scholz and R. Klinkenberg, “An Ensemble Classifier for Drifting Concepts,” Proc. Second Int’l Workshop Knowledge Discovery from Data Streams, pp. 53-64, 2005.

Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard

Pfahringer” MOA: massive online analysis”, 11(May):1601−1604, 2010.

J. Han and M. Kamber, “Data mining concepts and techniques”, Morgan Kaufmann, San Francisco 2006

Samuel Odei Danso,” An Exploration of Classification Prediction Techniques in Data Mining: The insurance domain”

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