research-article

An efficient algorithm for mining high utility patterns from incremental databases with one database scan

Authors:

Hamido FujitaAuthors Info & Claims

Knowledge-Based Systems, Volume 124, Issue C

Pages 188 - 206

https://doi.org/10.1016/j.knosys.2017.03.016

Published: 15 May 2017 Publication History

Abstract

High utility pattern mining has been actively researched as one of the significant topics in the data mining field since this approach can solve the limitation of traditional pattern mining that cannot fully consider characteristics of real world databases. Moreover, database volumes have been bigger gradually in various applications such as sales data of retail markets and connection information of web services, and general methods for static databases are not suitable for processing dynamic databases and extracting useful information from them. Although incremental utility pattern mining approaches have been suggested, previous approaches need at least two scans for incremental utility pattern mining irrespective of using any structure. However, the approaches with multiple scans are actually not adequate for stream environments. In this paper, we propose an efficient algorithm for mining high utility patterns from incremental databases with one database scan based on a list-based data structure without candidate generation. Experimental results with real and synthetic datasets show that the proposed algorithm outperforms previous one phase construction methods with candidate generation.

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cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 124, Issue C

May 2017

207 pages

ISSN:0950-7051

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 15 May 2017

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Cited By

Sun XNgueilbaye ALuo KCai YWu DHuang J(2024)A scalable and flexible basket analysis system for big transaction data in SparkInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10357761:2Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103577
Kim HKim HKim SKim HCho MVo BChun-Wei Lin JYun U(2023)An advanced approach for incremental flexible periodic pattern mining on time-series dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120697230:COnline publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120697
Mondal AMittal RSaurabh SChaudhary PReddy P(2023)An inventory-aware and revenue-based itemset placement framework for retail storesExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119404216:COnline publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119404
Han MLi MChen ZWu HZhang X(2023)High utility pattern mining algorithm over data streams using ext-list.Applied Intelligence10.1007/s10489-023-04925-653:22(27072-27095)Online publication date: 31-Aug-2023
https://dl.acm.org/doi/10.1007/s10489-023-04925-6
Li MHan MChen ZWu HZhang X(2023)FCHM-stream: fast closed high utility itemsets mining over data streamsKnowledge and Information Systems10.1007/s10115-023-01831-865:6(2509-2539)Online publication date: 3-Feb-2023
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Kim HRyu TLee CKim HYoon EVo BChun-Wei Lin JYun U(2022)EHMINExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118214209:COnline publication date: 15-Dec-2022
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Kannimuthu SChakravarthy D(2022)Discovery of Interesting Itemsets for Web Service Composition Using Hybrid Genetic AlgorithmNeural Processing Letters10.1007/s11063-022-10793-x54:5(3913-3939)Online publication date: 1-Oct-2022
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Saleti S(2022)Incremental mining of high utility sequential patterns using MapReduce paradigmCluster Computing10.1007/s10586-021-03448-425:2(805-825)Online publication date: 1-Apr-2022
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