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Constrained Frequent Pattern Mining from Big Data Via Crowdsourcing

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Big Data Applications and Services 2017 (BIGDAS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 770))

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Abstract

High volumes of wide varieties of valuable data of different veracities can be easily generated or collected at a high velocity from various big data applications and services. Embedded in these big data are valuable knowledge and useful information, which can be discovered by data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring items. Many of the existing frequent pattern mining algorithms return large numbers of frequent patterns, of which only a small portion may be of interest to users. In this paper, we present a constrained mining algorithm that allows crowds of users to collaboratively vote for their interesting patterns. Such an algorithm takes the benefits of crowdsourcing, crowdvoting and collaborative filtering for the data analytics and mining of popular constrained frequent patterns from big data applications and services.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Main_Page.

  2. 2.

    http://archive.ics.uci.edu/ml/datasets/online+retail.

  3. 3.

    https://www.surveymonkey.com/.

  4. 4.

    https://www.mturk.com/.

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Acknowledgements

This project is partially supported by (i) Natural Sciences and Engineering Research Council of Canada (NSERC) and (ii) University of Manitoba.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Calvin S. H. Hoi, Daniyal Khowaja & Carson K. Leung

Authors
  1. Calvin S. H. Hoi
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  2. Daniyal Khowaja
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  3. Carson K. Leung
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Corresponding author

Correspondence to Carson K. Leung .

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Editors and Affiliations

  1. Department of Industrial Engineering, Inha University, Incheon, Korea (Republic of)

    Wookey Lee

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

    Carson K. Leung

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Hoi, C.S.H., Khowaja, D., Leung, C.K. (2019). Constrained Frequent Pattern Mining from Big Data Via Crowdsourcing. In: Lee, W., Leung, C. (eds) Big Data Applications and Services 2017. BIGDAS 2017. Advances in Intelligent Systems and Computing, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-13-0695-2_9

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