Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6671
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dc.contributor.authorDarrab, Sadeq-
dc.contributor.authorErgenç, Belgin-
dc.date.accessioned2018-01-11T11:15:42Z-
dc.date.available2018-01-11T11:15:42Z-
dc.date.issued2017-
dc.identifier.citationDarrab, S., and Ergenç, B. (2017). Vertical pattern mining algorithm for multiple support thresholds. Procedia Computer Science, 112, 417-426. doi:10.1016/j.procs.2017.08.051en_US
dc.identifier.issn0877-0509-
dc.identifier.urihttp://doi.org/10.1016/j.procs.2017.08.051-
dc.identifier.urihttp://hdl.handle.net/11147/6671-
dc.description21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017; Saint Charles Campus of Aix-Marseille UniversityMarseille; France; 6 September 2017 through 8 September 2017en_US
dc.description.abstractFrequent pattern mining is an important task in discovering hidden items that co-occur (itemset) more than a predefined threshold in a database. Mining frequent itemsets has drawn attention although rarely occurring ones might have more interesting insights. In existing studies, to find these interesting patterns (rare itemsets), user defined single threshold should be set low enough but this results in generation of huge amount of redundant itemsets. We present Multiple Item Support-eclat; MIS-eclat algorithm, to mine frequent patterns including rare itemsets under multiple support thresholds (MIS) by utilizing a vertical representation of data. We compare MIS-eclat to our previous tree based algorithm, MISFP-growth28 and another recent algorithm, CFP-growth++22 in terms of execution time, memory usage and scalability on both sparse and dense databases. Experimental results reveal that MIS-eclat and MISFP-growth outperform CFP-growth++ in terms of execution time, memory usage and scalability.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK Project No: 114E779)en_US
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.relationinfo:eu-repo/grantAgreement/TUBITAK/EEEAG/114E779en_US
dc.relation.ispartofProcedia Computer Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFrequent pattern miningen_US
dc.subjectPattern growth treeen_US
dc.subjectVertical miningen_US
dc.subjectMultiple support thresholdsen_US
dc.titleVertical pattern mining algorithm for multiple support thresholdsen_US
dc.typeConference Objecten_US
dc.authoridTR130596en_US
dc.institutionauthorDarrab, Sadeq-
dc.institutionauthorErgenç, Belgin-
dc.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume112en_US
dc.identifier.startpage417en_US
dc.identifier.endpage426en_US
dc.identifier.wosWOS:000418466000043en_US
dc.identifier.scopus2-s2.0-85032386924en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.procs.2017.08.051-
dc.relation.doi10.1016/j.procs.2017.08.051en_US
dc.coverage.doi10.1016/j.procs.2017.08.051en_US
dc.identifier.scopusquality--
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextopen-
crisitem.author.dept03.04. Department of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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