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

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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.

Fabio Fumarola, Anna Ciampi, Annalisa Appice, Dona

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Data Stream | DIS 2009 | Frequent Pattern Mining | Frequent Patterns | Theoretical Computer Science |

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Added	26 May 2010
Updated	26 May 2010
Type	Conference
Year	2009
Where	DIS
Authors	Fabio Fumarola, Anna Ciampi, Annalisa Appice, Donato Malerba

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

Data Stream | DIS 2009 | Frequent Pattern Mining | Frequent Patterns | Theoretical Computer Science |

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