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10

ICDM
2007
IEEE

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Mining Frequent Itemsets in a Stream

13 years 11 months ago

Mining Frequent Itemsets in a Stream

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We study the problem of ﬁnding frequent itemsets in a continuous stream of transactions. The current frequency of an itemset in a stream is deﬁned as its maximal frequency over all possible windows in the stream from any point in the past until the current state that satisfy a minimal length constraint. Properties of this new measure are studied and an incremental algorithm that allows, at any time, to immediately produce the current frequencies of all frequent itemsets is proposed. Experimental and theoretical analysis show that the space requirements for the algorithm are extremely small for many realistic data distributions.

Toon Calders, Nele Dexters, Bart Goethals

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Data Mining | Frequent Itemsets | ICDM 2007 | Minimal Length Constraint | Stream |

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Added	03 Jun 2010
Updated	03 Jun 2010
Type	Conference
Year	2007
Where	ICDM
Authors	Toon Calders, Nele Dexters, Bart Goethals

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