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Sequential pattern mining using IDLists

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dc.title Sequential pattern mining using IDLists en
dc.contributor.author Huynh, Minh Huy
dc.contributor.author Pham, Ngoc Nam
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Nguyen, Loan Thi Thuy
dc.contributor.author Vo, Bay
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.identifier.issn 0302-9743 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-03-063006-5
dc.date.issued 2020
utb.relation.volume 12496 LNAI
dc.citation.spage 341
dc.citation.epage 353
dc.event.title 12th International Conference on Computational Collective Intelligence, ICCCI 2020
dc.event.location online
dc.event.sdate 2020-11-30
dc.event.edate 2020-12-03
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Science and Business Media Deutschland GmbH
dc.identifier.doi 10.1007/978-3-030-63007-2_27
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-030-63007-2_27
dc.subject Data-IDList en
dc.subject pseudo-IDList en
dc.subject sequential pattern mining en
dc.subject vertical format en
dc.description.abstract Sequential pattern mining is a practical problem whose objective is to discover helpful informative patterns in a stored database such as market transaction databases. It covers many applications in different areas. Recently, a study that improved the runtime for mining patterns was proposed. It was called pseudo-IDLists and it helps prevent duplicate data from replicating during the mining process. However, the idea only works for the special type of sequential patterns, which are clickstream patterns. Direct applying the idea for sequential pattern mining is not feasible. Hence, we proposed adaptions and changes to the novel idea and proposed SUI (Sequential pattern mining Using IDList), a sequential pattern mining algorithm based on pseudo-IDLists. Via experiments on three test databases, we show that SUI is efficient and effective regarding runtime and memory consumption. © 2020, Springer Nature Switzerland AG. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1010088
utb.identifier.obdid 43882277
utb.identifier.scopus 2-s2.0-85097540856
utb.source d-scopus
dc.date.accessioned 2020-12-22T08:40:37Z
dc.date.available 2020-12-22T08:40:37Z
utb.contributor.internalauthor Huynh, Minh Huy
utb.contributor.internalauthor Pham, Ngoc Nam
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.fulltext.affiliation Huy Minh Huynh 1, Nam Ngoc Pham 1, Zuzana Komínková Oplatková 1, Loan Thi Thuy Nguyen 2, and Bay Vo 3 1 Faculty of Applied Informatics, Tomas Bata University in Zlín, Nám. T.G. Masaryka, 5555 Zlín, Czech Republic {huynh,npham,oplatkova}@utb.cz 2 School of Computer Science and Engineering, International University - VNU-HCM, Ho Chi Minh City, Vietnam nttloan@hcmiu.edu.vn 3 Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam vd.bay@hutech.edu.vn
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported by the Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2020/001. The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab. fai.utb.cz).
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlín, Nám. T.G. Masaryka, Zlín, 5555, Czech Republic; School of Computer Science and Engineering, International University - VNU-HCM, Ho Chi Minh City, Viet Nam; Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam
utb.fulltext.projects IGA/CebiaTech/2020/001
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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