article

Finding efficiencies in frequent pattern mining from big uncertain data

Authors:

Carson Kai-Sang Leung,

Richard Kyle Mackinnon,

Fan JiangAuthors Info & Claims

World Wide Web, Volume 20, Issue 3

Pages 571 - 594

https://doi.org/10.1007/s11280-016-0411-3

Published: 01 May 2017 Publication History

Abstract

Many existing data mining algorithms search interesting patterns from transactional databases of precise data. However, there are situations in which data are uncertain. Items in each transaction of these probabilistic databases of uncertain data are usually associated with existential probabilities, which express the likelihood of these items to be present in the transaction. When compared with mining from precise data, the search space for mining from uncertain data is much larger due to the presence of the existential probabilities. This problem is worsened as we are moving to the era of Big data. Furthermore, in many real-life applications, users may be interested in a tiny portion of this large search space for Big data mining. Without providing opportunities for users to express the interesting patterns to be mined, many existing data mining algorithms return numerous patterns--out of which only some are interesting. In this article, we propose an algorithm that allows users to express their interest in terms of constraints, uses the MapReduce model to mine uncertain Big data for frequent patterns that satisfy the user-specified anti-monotone and monotone constraints, as well as balance the load.

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Cited By

Yu XLi QLiu J(2019)Scalable and parallel sequential pattern mining using sparkWorld Wide Web10.1007/s11280-018-0566-122:1(295-324)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11280-018-0566-1
Fan CHao HLeung CSun LTran J(2018)Social network mining for recommendation of friends based on music interestsProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382404(833-840)Online publication date: 28-Aug-2018
https://dl.acm.org/doi/10.5555/3382225.3382404
Leung CJiang F(2017)Efficient Mining of 'Following' Patterns from Very Big but Sparse Social NetworksProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3110089(1025-1032)Online publication date: 31-Jul-2017
https://dl.acm.org/doi/10.1145/3110025.3110089

Finding efficiencies in frequent pattern mining from big uncertain data
1. Information systems
  1. Information systems applications

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cover image World Wide Web

World Wide Web Volume 20, Issue 3

May 2017

155 pages

ISSN:1386-145X

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Copyright © Copyright © 2017 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 May 2017

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Cited By

Yu XLi QLiu J(2019)Scalable and parallel sequential pattern mining using sparkWorld Wide Web10.1007/s11280-018-0566-122:1(295-324)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11280-018-0566-1
Fan CHao HLeung CSun LTran J(2018)Social network mining for recommendation of friends based on music interestsProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382404(833-840)Online publication date: 28-Aug-2018
https://dl.acm.org/doi/10.5555/3382225.3382404
Leung CJiang F(2017)Efficient Mining of 'Following' Patterns from Very Big but Sparse Social NetworksProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3110089(1025-1032)Online publication date: 31-Jul-2017
https://dl.acm.org/doi/10.1145/3110025.3110089

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