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Mining Popular Patterns: A Novel Mining Problem and Its Application to Static Transactional Databases and Dynamic Data Streams

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXI

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9260))

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 (i) introduce popular patterns, which capture the popularity of individuals, items, or events among their peers or groups. Moreover, we also propose (ii) the Pop-tree structure to capture the essential information from transactional databases and (iii) the Pop-growth algorithm for mining popular patterns from the Pop-tree. Moreover, we illustrate how our algorithm (iv) mines popular friends from social networks. As we are not confined to mining popular patterns from static transactional databases, we extend our work to mining popular patterns from dynamic data streams. Specifically, we propose (v) the Pop-stream structure to capture the popular patterns in batches of data streams and (vi) the Pop-streaming algorithm for mining popular patterns from the Pop-stream structure. Experimental results showed that (i) our proposed tree structure is compact and space efficient and (ii) our proposed algorithm is time efficient in mining popular patterns from static transactional databases and dynamic data streams.

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Acknowledgement

This project is partially supported by (i) China Scholarship Council (CSC), (ii) Mitacs (Canada), (iii) Natural Sciences and Engineering Research Council of Canada (NSERC), and (iv) University of Manitoba.

Author information

Authors and Affiliations

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

    Alfredo Cuzzocrea

  2. University of Manitoba, Winnipeg, MB, Canada

    Fan Jiang, Carson K. Leung, Dacheng Liu, Aaron Peddle & Syed K. Tanbeer

  3. Wuhan University, Wuhan, Hubei, China

    Dacheng Liu

Authors
  1. Alfredo Cuzzocrea
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  2. Fan Jiang
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  3. Carson K. Leung
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  4. Dacheng Liu
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  5. Aaron Peddle
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  6. Syed K. Tanbeer
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Corresponding author

Correspondence to Carson K. Leung .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. ICAR-CNR and University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  5. Hewlett-Packard Labatories, Palo Alto, California, USA

    Umeshwar Dayal

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Cuzzocrea, A., Jiang, F., Leung, C.K., Liu, D., Peddle, A., Tanbeer, S.K. (2015). Mining Popular Patterns: A Novel Mining Problem and Its Application to Static Transactional Databases and Dynamic Data Streams. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXI. Lecture Notes in Computer Science(), vol 9260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47804-2_6

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  • DOI: https://doi.org/10.1007/978-3-662-47804-2_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47803-5

  • Online ISBN: 978-3-662-47804-2

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