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Efficient Skyline Itemsets Mining

Published: 13 July 2015 Publication History

Abstract

Utility Mining (UM) in context of Market Basket Analysis consists of mining itemsets from a transaction database guided by optimizing utility. For example, UM consists of extracting all itemsets in a transaction database having utility above a user-defined minimum threshold or mining Top-K high utility itemset. Similarly, Frequent Itemset Mining (FIM) finds frequent patterns using a frequency threshold. However, none of these pattern mining methods determine patterns that are interesting in both the aspects of utility and frequency. In addition these methods require a user to specify respective thresholds. In this paper, we present a novel framework for mining a new pattern called as Utility-Frequency Skyline Pattern. We formalize our problem as a pattern search problem and propose an efficient technique on recently proposed popular data structure called as UP Tree (Utility-Pattern Tree). The proposed algorithm consists of two phases called as Filter and Refine. In the Filter phase, a set of candidate itemsets are mined, which are then verified finally in the Refine phase. We study the effectiveness of our proposed algorithm along with two heuristics and conclude that our proposed method is efficient.

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Cited By

View all
  • (2024)Efficient Skyline Frequent-Utility Itemset Mining Algorithm on Massive DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.3349454(1-14)Online publication date: 2024
  • (2023)Mining skyline frequent-utility patterns from big data environment based on MapReduce frameworkIntelligent Data Analysis10.3233/IDA-22075627:5(1359-1377)Online publication date: 6-Oct-2023
  • (2023)The effective skyline quantify-utility patterns mining algorithm with pruning strategiesComputer Science and Information Systems10.2298/CSIS220615040W20:3(1085-1108)Online publication date: 2023
  • Show More Cited By

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    cover image ACM Other conferences
    C3S2E '15: Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering
    July 2015
    166 pages
    ISBN:9781450334198
    DOI:10.1145/2790798
    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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    • Keio University: Keio University
    • BytePress

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 July 2015

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    Author Tags

    1. Association Rule Mining
    2. FP-Tree
    3. Multi-Objective Optimization
    4. Utility-Based Data Mining
    5. Utility-Frequency Skyline Itemsets

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    • Short-paper
    • Research
    • Refereed limited

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    C3S2E 2015

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    Overall Acceptance Rate 12 of 42 submissions, 29%

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    Cited By

    View all
    • (2024)Efficient Skyline Frequent-Utility Itemset Mining Algorithm on Massive DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.3349454(1-14)Online publication date: 2024
    • (2023)Mining skyline frequent-utility patterns from big data environment based on MapReduce frameworkIntelligent Data Analysis10.3233/IDA-22075627:5(1359-1377)Online publication date: 6-Oct-2023
    • (2023)The effective skyline quantify-utility patterns mining algorithm with pruning strategiesComputer Science and Information Systems10.2298/CSIS220615040W20:3(1085-1108)Online publication date: 2023
    • (2023)FSKY-Miner: Fast Mining of Skyline Patterns2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00136(795-802)Online publication date: 21-Dec-2023
    • (2023)Mining Skyline Frequent-Utility Pattern with Threshold Filtering2023 8th International Conference on Computer and Communication Systems (ICCCS)10.1109/ICCCS57501.2023.10151123(138-143)Online publication date: 21-Apr-2023
    • (2023)Mining Skyline Patterns from Big Data Environments based on a Spark FrameworkJournal of Grid Computing10.1007/s10723-023-09653-221:2Online publication date: 5-Apr-2023
    • (2022)Semi-supervised incremental learning with few examples for discovering medical association rulesBMC Medical Informatics and Decision Making10.1186/s12911-022-01755-322:1Online publication date: 24-Jan-2022
    • (2022)Skyline Pattern Mining by Quantity-Utility Constraints in Large-Scale Databases2022 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW58026.2022.00076(547-552)Online publication date: Nov-2022
    • (2022)Effective algorithms to mine skyline frequent-utility itemsetsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105355116(105355)Online publication date: Nov-2022
    • (2022)Mining Frequency-Utility Patterns from a Big Data EnvironmentAdvances in Intelligent Systems and Computing10.1007/978-981-16-8048-9_6(53-61)Online publication date: 22-Feb-2022
    • Show More Cited By

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