short-paper

Mining probabilistic datasets vertically

Authors:

Carson Kai-Sang Leung,

Syed K. Tanbeer,

Bhavek P. Budhia,

Lauren C. ZachariasAuthors Info & Claims

IDEAS '12: Proceedings of the 16th International Database Engineering & Applications Sysmposium

Pages 199 - 204

https://doi.org/10.1145/2351476.2351500

Published: 08 August 2012 Publication History

Abstract

As frequent pattern mining plays an important role in various real-life applications, it has been the subject of numerous studies. Most of the studies mine transactional datasets of precise data. However, there are situations in which data are uncertain. Over the few years, Apriori-based, tree-based, and hyperlinked array structure based mining algorithms have been proposed to mine frequent patterns from these probabilistic datasets of uncertain data. These algorithms view the datasets "horizontally" as collections of transactions, and each records a set of items contained in that transaction. In this paper, we consider an alternative representation such that probabilistic datasets of uncertain data can be viewed "vertically" as collections of vectors. The vector for each item indicates which transactions contain that item. We also propose an algorithm called U-VIPER to mine these probabilistic datasets "vertically for frequent patterns.

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Cited By

Czubryt TLeung CPazdor A(2022)Q-Eclat: Vertical Mining of Interesting Quantitative PatternsProceedings of the 26th International Database Engineered Applications Symposium10.1145/3548785.3548808(25-33)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1145/3548785.3548808
Leung CFung DMai DWen QTran JSouza J(2021)Explainable Data Analytics for Disease and Healthcare InformaticsProceedings of the 25th International Database Engineering & Applications Symposium10.1145/3472163.3472175(65-74)Online publication date: 14-Jul-2021
https://dl.acm.org/doi/10.1145/3472163.3472175
Davashi R(2021)UP-tree & UP-MineEngineering Applications of Artificial Intelligence10.1016/j.engappai.2021.104477106:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.engappai.2021.104477
Show More Cited By

Index Terms

Mining probabilistic datasets vertically
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Other conferences

IDEAS '12: Proceedings of the 16th International Database Engineering & Applications Sysmposium

August 2012

261 pages

ISBN:9781450312349

DOI:10.1145/2351476

General Chair:
Bipin C. Desai
Concordia University, Canada
,
Program Chairs:
Jaroslav Pokorny
Charles University Prague, Czech Repubilc
,
Jorge Bernardino
Instituto Politecnico de Coimbra, Portugal

Copyright © 2012 ACM.

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Charles University: Charles University
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Concordia University: Concordia University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 August 2012

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IDEAS '12

Sponsor:

Charles University
Concordia University

IDEAS '12: 16th International Database Engineering & Applications Symposium

August 8 - 10, 2012

Prague, Czech Republic

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Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

Czubryt TLeung CPazdor A(2022)Q-Eclat: Vertical Mining of Interesting Quantitative PatternsProceedings of the 26th International Database Engineered Applications Symposium10.1145/3548785.3548808(25-33)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1145/3548785.3548808
Leung CFung DMai DWen QTran JSouza J(2021)Explainable Data Analytics for Disease and Healthcare InformaticsProceedings of the 25th International Database Engineering & Applications Symposium10.1145/3472163.3472175(65-74)Online publication date: 14-Jul-2021
https://dl.acm.org/doi/10.1145/3472163.3472175
Davashi R(2021)UP-tree & UP-MineEngineering Applications of Artificial Intelligence10.1016/j.engappai.2021.104477106:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.engappai.2021.104477
Gupta PHoi CLeung CYuan YZhang XZhang Z(2020)Vertical Data Mining from Relational Data and Its Application to COVID-19 DataBig Data Analyses, Services, and Smart Data10.1007/978-981-15-8731-3_8(106-116)Online publication date: 11-Sep-2020
https://doi.org/10.1007/978-981-15-8731-3_8
Choudhery DLeung CDesai BHong J(2017)Social Media MiningProceedings of the 21st International Database Engineering & Applications Symposium10.1145/3105831.3105854(20-29)Online publication date: 12-Jul-2017
https://dl.acm.org/doi/10.1145/3105831.3105854
Leung CCuzzocrea ADesai BToyoma M(2015)Frequent Subgraph Mining from Streams of Uncertain DataProceedings of the Eighth International C* Conference on Computer Science & Software Engineering10.1145/2790798.2790799(18-27)Online publication date: 13-Jul-2015
https://dl.acm.org/doi/10.1145/2790798.2790799
Leung CMacKinnon RWang YDesai BBernardino JAlmeida ADesai B(2014)A machine learning approach for stock price predictionProceedings of the 18th International Database Engineering & Applications Symposium10.1145/2628194.2628211(274-277)Online publication date: 7-Jul-2014
https://dl.acm.org/doi/10.1145/2628194.2628211
Leung C(2014)Uncertain Frequent Pattern MiningFrequent Pattern Mining10.1007/978-3-319-07821-2_14(339-367)Online publication date: 30-Aug-2014
https://doi.org/10.1007/978-3-319-07821-2_14
Cameron JCuzzocrea ALeung CShin SMaldonado J(2013)Stream mining of frequent sets with limited memoryProceedings of the 28th Annual ACM Symposium on Applied Computing10.1145/2480362.2480398(173-175)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.1145/2480362.2480398
Jiang FLeung C(2013)Stream Mining of Frequent Patterns from Delayed Batches of Uncertain DataProceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery - Volume 805710.1007/978-3-642-40131-2_18(209-221)Online publication date: 26-Aug-2013
https://dl.acm.org/doi/10.1007/978-3-642-40131-2_18
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