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Data Streams pp 61–84Cite as

Frequent Pattern Mining in Data Streams

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Part of the book series: Advances in Database Systems ((ADBS,volume 31))

Abstract

Frequent pattern mining is a core data mining operation and has been extensively studied over the last decade. Recently, mining frequent patterns over data streams have attracted a lot of research interests. Compared with other streaming queries, frequent pattern mining poses great challenges due to high memory and computational costs, and accuracy requirement of the mining results.

In this chapter, we overview the state-of-art techniques to mine frequent patterns over data streams. We also introduce a new approach for this problem, which makes two major contributions. First, this one pass algorithm for frequent itemset mining has deterministic bounds on the accuracy, and does not require any out-of-core summary structure. Second, because the one pass algorithm does not produce any false negatives, it can be easily extended to a two pass accurate algorithm. The two pass algorithm is very memory efficient.

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Author information

Authors and Affiliations

  1. Computer Science Department, Kent State University, USA

    Ruoming Jin

  2. Department of Computer Science and Engineering, The Ohio State University, USA

    Gagan Agrawal

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  1. Ruoming Jin
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  2. Gagan Agrawal
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Editor information

Editors and Affiliations

  1. IBM, Thomas J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY, 10532

    Charu C. Aggarwal

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© 2007 Springer Science+Business Media, LLC

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Jin, R., Agrawal, G. (2007). Frequent Pattern Mining in Data Streams. In: Aggarwal, C.C. (eds) Data Streams. Advances in Database Systems, vol 31. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-47534-9_4

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  • DOI: https://doi.org/10.1007/978-0-387-47534-9_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-28759-1

  • Online ISBN: 978-0-387-47534-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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