Title
Mining frequent itemsets in a stream Mining frequent itemsets in a stream
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
conferenceObject
Publication
Los Alamitos, Calif. :IEEE Computer Society, [*]
Subject
Computer. Automation
Source (book)
Proceedings of the IEEE International Conference on Data Mining 2007
ISSN
1550-4786
ISI
000253429400009
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
Abstract
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.
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