Abstract
When we mine information for knowledge on a whole data streams it’s necessary to cope with uncertainty as only a part of the stream is available. We introduce a stastistical technique, independant from the used algorithm, for estimating the frequent itemset on a stream. This statistical support allows to maximize either the precision or the recall as choosen by the user, while it doesn’t damage the other. Experiments with various association rules databases demonstrate the potential of such technique.
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Laur, PA., Symphor, JE., Nock, R., Poncelet, P. (2005). Statistical Supports for Frequent Itemsets on Data Streams. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_39
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DOI: https://doi.org/10.1007/11510888_39
Publisher Name: Springer, Berlin, Heidelberg
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