Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered: data streams describe complex objects modeled by multiple database relations. A multi-relational data mining algorithm is proposed to efficiently discover approximate relational frequent patterns over a sliding time window of a complex data stream. The effectiveness of the method is proved on application to the Internet packet stream.

A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams

APPICE, ANNALISA;MALERBA, Donato
2009-01-01

Abstract

Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered: data streams describe complex objects modeled by multiple database relations. A multi-relational data mining algorithm is proposed to efficiently discover approximate relational frequent patterns over a sliding time window of a complex data stream. The effectiveness of the method is proved on application to the Internet packet stream.
2009
978-3-642-04746-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/74615
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