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International Conference on Data Warehousing and Knowledge Discovery

DaWaK 2012: Data Warehousing and Knowledge Discovery pp 291–302Cite as

Mining Popular Patterns from Transactional Databases

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7448)

Abstract

Since the introduction of the frequent pattern mining problem, researchers have extended frequent patterns to different useful patterns such as cyclic, emerging, periodic and regular patterns. In this paper, we introduce popular patterns, which captures the popularity of individuals, items, or events among their peers or groups. Moreover, we also propose (i) the Pop-tree structure to capture the essential information for the mining of popular patterns and (ii) the Pop-growth algorithm for mining popular patterns. Experimental results showed that our proposed tree structure is compact and space efficient and our proposed algorithm is time efficient.

Keywords

  • Data mining
  • knowledge discovery
  • interesting patterns
  • popular patterns
  • useful patterns
  • tree-based mining

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  • DOI: 10.1007/978-3-642-32584-7_24
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Author information

Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Carson Kai-Sang Leung & Syed K. Tanbeer

Authors
  1. Carson Kai-Sang Leung
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  2. Syed K. Tanbeer
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Editor information

Editors and Affiliations

  1. ICAR-CNR and University of Calabria, via P. Bucci 41C, 87036, Rende (CS), Italy

    Alfredo Cuzzocrea

  2. Hewlett Packard Labs, 1501 Page Mill Road, MS 1142, 94304, Palo Alto, CA, USA

    Umeshwar Dayal

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Leung, C.KS., Tanbeer, S.K. (2012). Mining Popular Patterns from Transactional Databases. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32584-7_24

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  • DOI: https://doi.org/10.1007/978-3-642-32584-7_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32583-0

  • Online ISBN: 978-3-642-32584-7

  • eBook Packages: Computer ScienceComputer Science (R0)