Article,

Finding frequent itemsets over online data streams

, and .
Information and Software Technology, 48 (7): 606--618 (July 2006)
DOI: 10.1016/j.infsof.2005.06.004

Abstract

Conventional data mining methods for finding frequent itemsets require considerable computing time to produce their results from a large data set. Due to this reason, it is almost impossible to apply them to an analysis task in an online data stream where a new transaction is continuously generated at a rapid rate. An algorithm for finding frequent itemsets over an online data stream should support flexible trade-off between processing time and mining accuracy. Furthermore, the most up-to-date resulting set of frequent itemsets should be available quickly at any moment. To satisfy these requirements, this paper proposes a data mining method f…(more)

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