research-article

Erasable itemset mining over incremental databases with weight conditions

Authors:

Donggyu KimAuthors Info & Claims

Engineering Applications of Artificial Intelligence, Volume 52, Issue C

Pages 213 - 234

https://doi.org/10.1016/j.engappai.2016.03.003

Published: 01 June 2016 Publication History

Abstract

Erasable itemset mining is an approach for mining itemsets with low profits from large-scale product databases in order to solve financial crises of plants in manufacturing industries. Previous erasable itemset mining methods deal with static product databases only, and ignore any characteristics such as items' own values when they extract the erasable itemsets. Therefore, such approaches may fail to solve financial crises of plants because they have to iterate a significant number of mining processes in order to deal with real-time product data accumulated from plants in the real world. In this paper, we propose a new tree-based erasable itemset mining algorithm for dynamic databases, which finds erasable itemsets considering the weight conditions from incremental databases. The proposed algorithm uses new tree and list data structures for performing its mining operations more efficiently. Furthermore, the proposed algorithm is capable of reducing the number of mined erasable itemsets by considering the different weight information of items within product databases. We also compare the proposed approach with other tree-based state-of-the-art methods. By performing runtime, memory, pattern quality, and scalability comparisons with respect to various real and synthetic incremental datasets, we show that the proposed algorithm is outstanding in comparison to other previous methods.

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Cited By

Hong TChen DHuang WTsai Y(2024)Mining Erasable Itemsets from a Product Database with Quantitative ComponentsProceedings of the 2024 11th Multidisciplinary International Social Networks Conference10.1145/3675669.3675698(62-66)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1145/3675669.3675698
Hong TLi JTsai YHuang W(2023)Tree-Based Unified Temporal Erasable-Itemset MiningIntelligent Information and Database Systems10.1007/978-981-99-5834-4_18(224-233)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-5834-4_18
Baek YYun ULin JYoon EFujita H(2020)Efficiently mining erasable stream patterns for intelligent systems over uncertain dataInternational Journal of Intelligent Systems10.1002/int.2226935:11(1699-1734)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1002/int.22269
Show More Cited By

Index Terms

Erasable itemset mining over incremental databases with weight conditions
1. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 52, Issue C

June 2016

248 pages

ISSN:0952-1976

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 June 2016

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Cited By

Hong TChen DHuang WTsai Y(2024)Mining Erasable Itemsets from a Product Database with Quantitative ComponentsProceedings of the 2024 11th Multidisciplinary International Social Networks Conference10.1145/3675669.3675698(62-66)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1145/3675669.3675698
Hong TLi JTsai YHuang W(2023)Tree-Based Unified Temporal Erasable-Itemset MiningIntelligent Information and Database Systems10.1007/978-981-99-5834-4_18(224-233)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-5834-4_18
Baek YYun ULin JYoon EFujita H(2020)Efficiently mining erasable stream patterns for intelligent systems over uncertain dataInternational Journal of Intelligent Systems10.1002/int.2226935:11(1699-1734)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1002/int.22269
Le TVo BFournier-Viger PLee MBaik S(2019)SPPCApplied Intelligence10.1007/s10489-018-1280-549:2(478-495)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10489-018-1280-5
Huynh BVo B(2018)An Efficient Method for Mining Erasable Itemsets Using Multicore Processor PlatformComplexity10.1155/2018/84876412018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/8487641
Hong TLi CWang SLin C(2018)Maintenance of Erasable Itemsets for Product DeletionProceedings of the 5th Multidisciplinary International Social Networks Conference10.1145/3227696.3227718(1-4)Online publication date: 16-Jul-2018
https://dl.acm.org/doi/10.1145/3227696.3227718
Lee GYun U(2018)Single-pass based efficient erasable pattern mining using list data structure on dynamic incremental databasesFuture Generation Computer Systems10.1016/j.future.2017.07.03580:C(12-28)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.future.2017.07.035
(2018)Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume conceptEngineering Applications of Artificial Intelligence10.1016/j.engappai.2017.09.01068:C(1-9)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.engappai.2017.09.010
Yun URyang HLee GFujita H(2017)An efficient algorithm for mining high utility patterns from incremental databases with one database scanKnowledge-Based Systems10.1016/j.knosys.2017.03.016124:C(188-206)Online publication date: 15-May-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.03.016
Kim DYun U(2017)Efficient algorithm for mining high average-utility itemsets in incremental transaction databasesApplied Intelligence10.1007/s10489-016-0890-z47:1(114-131)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10489-016-0890-z

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