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TNS: mining top-k non-redundant sequential rules

Published: 18 March 2013 Publication History

Abstract

Mining sequential rules from sequence databases is an important research problem with wide applications. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. Moreover, a large proportion of sequential rules generated are redundant. In previous works, these two problems have been addressed separately. In this paper, we address both by proposing an algorithm for mining top-k non redundant sequential rules.

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Fournier-Viger, P. Gueniche, T. and Tseng, V. S. (2012). Using Partially-Ordered Sequential Rules to Generate More Accurate Sequence Prediction. Proc. ADMA 2012, pp.431--442.
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  • (2023)Sequential Rule Mining for Automated Design of Meta-heuristicsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596303(1727-1735)Online publication date: 15-Jul-2023
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    cover image ACM Conferences
    SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing
    March 2013
    2124 pages
    ISBN:9781450316569
    DOI:10.1145/2480362
    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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    Publication History

    Published: 18 March 2013

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    Author Tags

    1. algorithm
    2. redundancy
    3. sequential rules
    4. top-k pattern mining

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    March 18 - 22, 2013
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    SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    Cited By

    View all
    • (2023)Sequential Rule Mining for Automated Design of Meta-heuristicsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596303(1727-1735)Online publication date: 15-Jul-2023
    • (2023)US-Rule: Discovering Utility-driven Sequential RulesACM Transactions on Knowledge Discovery from Data10.1145/353261317:1(1-22)Online publication date: 20-Feb-2023
    • (2022)A Sequential Pattern Mining Approach to Tourist Movement: The Case of a Mega EventJournal of Travel Research10.1177/0047287522112643362:6(1237-1256)Online publication date: 15-Oct-2022
    • (2022)Learning to Ask: Conversational Product Search via Representation LearningACM Transactions on Information Systems10.1145/355537141:2(1-27)Online publication date: 21-Dec-2022
    • (2022)Cache-oblivious Hilbert Curve-based Blocking Scheme for Matrix TranspositionACM Transactions on Mathematical Software10.1145/355535348:4(1-28)Online publication date: 19-Dec-2022
    • (2021)Automated Business Process Discovery from Unstructured Natural-Language DocumentsBusiness Process Management Workshops10.1007/978-3-030-66498-5_18(232-243)Online publication date: 19-Jan-2021
    • (2020)A System for Predictive Data Analytics Using Sequential Rule MiningInternational Journal of Software Innovation10.4018/IJSI.20201001078:4(78-94)Online publication date: Oct-2020
    • (2019)Extracting Workflows from Natural Language Documents: A First StepBusiness Process Management Workshops10.1007/978-3-030-11641-5_23(294-300)Online publication date: 29-Jan-2019
    • (2018)Flexible Learning with Semantic Visual Exploration and Sequence-Based Recommendation of MOOC VideosProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3173903(1-13)Online publication date: 21-Apr-2018
    • (2018)Improved behavior model based on sequential rule miningApplied Soft Computing10.1016/j.asoc.2018.01.03568(944-960)Online publication date: Jul-2018
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