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Efficient Frequent Itemset Mining from Dense Data Streams

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Web Technologies and Applications (APWeb 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8709))

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Abstract

Due to advances in technology, high volumes of valuable data can be produced at high velocity in many real-life applications. Hence, efficient data mining techniques for discovering implicit, previously unknown, and potentially useful frequent itemsets from data streams are in demand. Many existing stream mining algorithms capture important stream data and assume that the captured data can fit into main memory. However, problems arise when the available memory is so limited that such an assumption does not hold. In this paper, we present a data structure to capture important data from the streams onto the disk. In addition, we present two algorithms—which use this data structure—to mine frequent itemsets from these dense (or sparse) data streams.

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Author information

Authors and Affiliations

  1. ICAR-CNR & University of Calabria, Rende, CS, Italy

    Alfredo Cuzzocrea

  2. University of Manitoba, Winnipeg, MB, Canada

    Fan Jiang & Carson K. Leung

  3. Inha University, Incheon, South Korea

    Wookey Lee

Authors
  1. Alfredo Cuzzocrea
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  2. Fan Jiang
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  3. Wookey Lee
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  4. Carson K. Leung
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Editor information

Editors and Affiliations

  1. Beijing Institute of Spacecraft System Engineering, Beijing, China

    Lei Chen

  2. School of Computer Science, National University of Defense Technology, 410073, Changsha, Hunan, China

    Yan Jia

  3. RMIT University, Melbourne, Australia

    Timos Sellis

  4. School of Computer Science and Technology, Soochow University, 215006, Suzhou, China

    Guanfeng Liu

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© 2014 Springer International Publishing Switzerland

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Cuzzocrea, A., Jiang, F., Lee, W., Leung, C.K. (2014). Efficient Frequent Itemset Mining from Dense Data Streams. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_56

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  • DOI: https://doi.org/10.1007/978-3-319-11116-2_56

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11115-5

  • Online ISBN: 978-3-319-11116-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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