Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis

DOI10.1007/S10618-010-0204-8zbMath1235.68202OpenAlexW2091806949MaRDI QIDQ408650

Arbee L. P. Chen, En Tzu Wang

Publication date: 11 April 2012

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-010-0204-8

zbMATH Keywords

data miningcontinuous distributed modeldistributed data streamsfrequent itemsethash-based approach

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

A MapReduce-based parallel frequent pattern growth algorithm for spatiotemporal association analysis of mobile trajectory big data ⋮ Adaptive fuzzy partitions for evolving association rules in big data stream

Cites Work

• An improved data stream summary: the count-min sketch and its applications
• Database Theory - ICDT 2005
• Unnamed Item

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