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Uncertain Frequent Pattern Mining

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Frequent Pattern Mining

Abstract

Frequent pattern mining aims to discover implicit, previously unknown and potentially useful knowledge—in the form of frequently occurring sets of items—that are embedded in data. Many of the models and algorithms developed in the early days mine frequent patterns from traditional transaction databases of precise data such as shopper market basket data, in which the contents of databases are known. However, we are living in an uncertain world, in which uncertain data can be found in various real-life applications. Hence, in recent years, researchers have paid more attention to frequent pattern mining from probabilistic datasets of uncertain data. This chapter covers key models, algorithms and topics about uncertain frequent pattern mining.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Carson Kai-Sang Leung

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  1. Carson Kai-Sang Leung
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Correspondence to Carson Kai-Sang Leung .

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Editors and Affiliations

  1. IBM, Yorktown Heights, New York, USA

    Charu C. Aggarwal

  2. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Jiawei Han

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Leung, CS. (2014). Uncertain Frequent Pattern Mining. In: Aggarwal, C., Han, J. (eds) Frequent Pattern Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-07821-2_14

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  • DOI: https://doi.org/10.1007/978-3-319-07821-2_14

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  • Print ISBN: 978-3-319-07820-5

  • Online ISBN: 978-3-319-07821-2

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