Uncertainty Oriented-Incremental Erasable Pattern Mining Over Data Streams

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorKim, Hanju
dc.contributor.authorCho, Myungha
dc.contributor.authorKim, Hyeonmo
dc.contributor.authorBaek, Yoonji
dc.contributor.authorLee, Chanhee
dc.contributor.authorRyu, Taewoong
dc.contributor.authorKim, Heonho
dc.contributor.authorPark, Seungwan
dc.contributor.authorKim, Doyoon
dc.contributor.authorKim, Doyoung
dc.contributor.authorKim, Sinyoung
dc.contributor.authorVo, Bay
dc.contributor.authorLin, Jerry Chun-Wei
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYun, Unil
dc.date.accessioned2025-04-18T08:51:55Z
dc.date.available2025-04-18T08:51:55Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Lisansüstü Eğitim Enstitüsü, Fen Bilimleri, Bilgisayar Mühendisliği Programı
dc.description.abstractIn a manufacturing factory, product lines are organized by several constituents and exhibit a profit value, i.e., income from products. Erasable patterns are less profitable patterns whose gain, i.e., the sum of product profits, does not exceed a user-defined threshold. Mining erasable patterns provides the necessary information to users who want to increase profits by erasing less profitable patterns. There are requirements for a method which efficiently manages uncertain databases in incremental environments to identify erasable patterns that consider uncertainty. Because our novel technique uses a list structure, it is more efficient at finding erasable patterns from incremental databases. Moreover, accumulated stream data should be handled efficiently to identify new useful patterns in both additional data and the existing data. In this article, an algorithm using a list-based structure is proposed to extract erasable patterns containing valuable knowledge from uncertain databases in real time with effective and productive performance. In order to derive erasable patterns from continuously accumulated stream databases, the structure efficiently manages the information gathered from the previous database. Extensive performance and pattern quality evaluations were conducted using real and synthetic datasets. The results show that the algorithm performs up to seven times faster than state-of-the-art erasable pattern mining algorithms on real datasets and scales adeptly on synthetic datasets while delivering reliable and significant result patterns. © 2013 IEEE.
dc.description.sponsorshipThis workwas supported by the National Research Foundation of Korea (NRF) fundedby the Ministry of Education, Science and Technology under Grant NRF2021R1A2C1009388.
dc.identifier.citationKim, H., Cho, M., Kim, H., Baek, Y., Lee, C., Ryu, T., ... & Yun, U. (2024). Uncertainty oriented-incremental erasable pattern mining over data streams. IEEE Transactions on Systems, Man, and Cybernetics: Systems.
dc.identifier.doi10.1109/TSMC.2024.3505904
dc.identifier.endpage1465
dc.identifier.issn21682216
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85212245981
dc.identifier.scopusqualityQ1
dc.identifier.startpage1451
dc.identifier.urihttp://dx.doi.org/10.1109/TSMC.2024.3505904
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6631
dc.identifier.volume55
dc.identifier.wosWOS:001377387500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData Streams
dc.subjectErasable Pattern Mining (EPM)
dc.subjectİncremental Mining
dc.subjectUncertainty
dc.titleUncertainty Oriented-Incremental Erasable Pattern Mining Over Data Streams
dc.typeArticle

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