demonstration

UFIMT: an uncertain frequent itemset mining toolbox

Authors:

Yongxin Tong,

Lei Chen,

Philip S. YuAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1508 - 1511

https://doi.org/10.1145/2339530.2339767

Published: 12 August 2012 Publication History

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Abstract

In recent years, mining frequent itemsets over uncertain data has attracted much attention in the data mining community. Unlike the corresponding problem in deterministic data, the frequent itemset under uncertain data has two different definitions: the expected support-based frequent itemset and the probabilistic frequent itemset. Most existing works only focus on one of the definitions and no comprehensive study is conducted to compare the two different definitions. Moreover, due to lacking the uniform implementation platform, existing solutions for the same definition even generate inconsistent results. In this demo, we present a demonstration called as UFIMT (underline Uncertain Frequent Itemset Mining Toolbox) which not only discovers frequent itemsets over uncertain data but also compares the performance of different algorithms and demonstrates the relationship between different definitions. In this demo, we firstly present important techniques and implementation skills of the mining problem, secondly, we show the system architecture of UFIMT, thirdly, we report an empirical analysis on extensive both real and synthetic benchmark data sets, which are used to compare different algorithms and to show the close relationship between two different frequent itemset definitions, and finally we discuss some existing challenges and new findings.

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Cited By

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Miao RZhong ZGuo JLi ZYang TCui B(2023)BurstSketch: Finding Bursts in Data StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322368635:11(11126-11140)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TKDE.2022.3223686
Wazir SBeg MAhmad T(2021)A Review on Frequent Itemsets Generation Techniques and Their Comparative Analysis Using FIMAKSN Computer Science10.1007/s42979-021-00916-x3:1Online publication date: 23-Oct-2021
https://doi.org/10.1007/s42979-021-00916-x
Wazir SBeg MAhmad T(2019)Comprehensive mining of frequent itemsets for a combination of certain and uncertain databasesInternational Journal of Information Technology10.1007/s41870-019-00310-0Online publication date: 30-Apr-2019
https://doi.org/10.1007/s41870-019-00310-0
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Index Terms

UFIMT: an uncertain frequent itemset mining toolbox
1. Information systems
  1. Information systems applications
    1. Data mining
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures

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Information & Contributors

Information

Published In

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2012

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Demonstration

Conference

KDD '12

Sponsor:

KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all

Miao RZhong ZGuo JLi ZYang TCui B(2023)BurstSketch: Finding Bursts in Data StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322368635:11(11126-11140)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TKDE.2022.3223686
Wazir SBeg MAhmad T(2021)A Review on Frequent Itemsets Generation Techniques and Their Comparative Analysis Using FIMAKSN Computer Science10.1007/s42979-021-00916-x3:1Online publication date: 23-Oct-2021
https://doi.org/10.1007/s42979-021-00916-x
Wazir SBeg MAhmad T(2019)Comprehensive mining of frequent itemsets for a combination of certain and uncertain databasesInternational Journal of Information Technology10.1007/s41870-019-00310-0Online publication date: 30-Apr-2019
https://doi.org/10.1007/s41870-019-00310-0
Wang LYang TWang HJiang JCai ZCui BLi X(2019)Fine-grained probability counting for cardinality estimation of data streamsWorld Wide Web10.1007/s11280-018-0583-022:5(2065-2081)Online publication date: 2-Aug-2019
https://dl.acm.org/doi/10.1007/s11280-018-0583-0
Negi PWazir STabrez Nafis M(2019)Discovering Frequent High Average Utility Itemset Without Transaction InsertionSustainable Communication Networks and Application10.1007/978-3-030-34515-0_58(555-569)Online publication date: 7-Nov-2019
https://doi.org/10.1007/978-3-030-34515-0_58
Wazir SSufyan Beg MAhmad T(2018)Frequent Itemset Mining on Uncertain Database Using OWA OperatorProceedings of 2nd International Conference on Communication, Computing and Networking10.1007/978-981-13-1217-5_75(745-755)Online publication date: 8-Sep-2018
https://doi.org/10.1007/978-981-13-1217-5_75
Wazir SAhmad TSufyan Beg M(2018)Frequent Itemset Mining for a Combination of Certain and Uncertain DatabasesRecent Developments and the New Direction in Soft-Computing Foundations and Applications10.1007/978-3-319-75408-6_3(25-39)Online publication date: 29-May-2018
https://doi.org/10.1007/978-3-319-75408-6_3
Tong YZhang XChen L(2016)Tracking frequent items over distributed probabilistic dataWorld Wide Web10.1007/s11280-015-0341-519:4(579-604)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s11280-015-0341-5
Lan YWang YWang YYi SYu D(2015)Mining High Utility Itemsets over Uncertain DatabasesProceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery10.1109/CyberC.2015.76(235-238)Online publication date: 17-Sep-2015
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Liang FLiu YLiu HMa SSchnor B(2015)A Parallel Job Execution Time Estimation Approach Based on User Submission Patterns within Computational GridsInternational Journal of Parallel Programming10.1007/s10766-013-0294-143:3(440-454)Online publication date: 1-Jun-2015
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