Mining Evolving data Streams for Frequent Patterns

Abstract : A data stream is a potentially uninterrupted flow of data. Mining this flow makes it necessary to cope with uncertainty, as only a part of the stream can be stored. In this paper, we evaluate a statistical technique which biases the estimation of the support of patterns, so as to maximize either the precision or the recall, as chosen by the user, and limit the degradation of the other criterion. Theoretical results show that the technique is not far from the optimum, from the statistical standpoint. Experiments performed tend to demonstrate its potential, as it remains robust even under significant distribution drifts.
Type de document :
Article dans une revue
Pattern Recognition, Elsevier, 2007, 40, pp.492-503
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Contributeur : Pascal Poncelet <>
Soumis le : mardi 3 mars 2009 - 15:11:33
Dernière modification le : mercredi 18 juillet 2018 - 20:11:27


  • HAL Id : lirmm-00365474, version 1



Pierre-Alain Laur, Richard Nock, Jean-Émile Symphor, Pascal Poncelet. Mining Evolving data Streams for Frequent Patterns. Pattern Recognition, Elsevier, 2007, 40, pp.492-503. 〈lirmm-00365474〉



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