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Computing Theoretically-Sound Upper Bounds to Expected Support for Frequent Pattern Mining Problems over Uncertain Big Data

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 611))

Abstract

Frequent pattern mining aims to discover implicit, previously unknown, and potentially useful knowledge in the form of sets of frequently co-occurring items, events, or objects. To mine frequent patterns from probabilistic datasets of uncertain data, where each item in a transaction is usually associated with an existential probability expressing the likelihood of its presence in that transaction, the UF-growth algorithm captures important information about uncertain data in a UF-tree structure so that expected support can be computed for each pattern. A pattern is considered frequent if its expected support meets or exceeds the user-specified threshold. However, a challenge is that the UF-tree can be large. To handle this challenge, several algorithms use smaller trees such that upper bounds to expected support can be computed. In this paper, we examine these upper bounds, and determine which ones provide tighter upper bounds to expected support for frequent pattern mining of uncertain big data.

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Acknowledgements

This project is partially supported by NSERC (Canada) and University of Manitoba.

Author information

Authors and Affiliations

  1. DIA Department, University of Trieste and ICAR-CNR, Trieste, TS, Italy

    Alfredo Cuzzocrea

  2. Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada

    Carson K. Leung

Authors
  1. Alfredo Cuzzocrea
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  2. Carson K. Leung
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Corresponding author

Correspondence to Carson K. Leung .

Editor information

Editors and Affiliations

  1. INESC-ID,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Joao Paulo Carvalho

  2. LIP 6, Université Pierre et Marie Curie, Paris, France

    Marie-Jeanne Lesot

  3. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Uzay Kaymak

  4. IDMEC,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Susana Vieira

  5. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  6. Iona College, Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

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Cuzzocrea, A., Leung, C.K. (2016). Computing Theoretically-Sound Upper Bounds to Expected Support for Frequent Pattern Mining Problems over Uncertain Big Data. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-40581-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-40581-0_31

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