Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/2355
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dc.contributor.authorBelkadi, Widad Hassina-
dc.contributor.authorDrias, Yassine-
dc.contributor.authorDrias, Habiba-
dc.date.accessioned2024-10-05T18:38:43Z-
dc.date.available2024-10-05T18:38:43Z-
dc.date.issued2024-
dc.identifier.isbn9783031602740-
dc.identifier.isbn9783031593185-
dc.identifier.isbn9783031593178-
dc.identifier.issn3004-958X-
dc.identifier.urihttps://doi.org/10.1007/978-3-031-59318-5_8-
dc.identifier.urihttps://hdl.handle.net/20.500.11779/2355-
dc.description.abstractQuantum computing, based on quantum mechanics, promises revolutionary computational power by exploiting quantum states. It provides significant advantages over classical computing regarding time complexity, enabling faster and more efficient problem-solving. This paper explores the application of quantum computing in frequent itemset mining and association rules mining, a crucial task in data mining and pattern recognition. We propose a novel algorithm called Quantum FP-Growth (QFP-Growth) for mining frequent itemsets. The QFP-Growth algorithm follows the traditional FP-Growth approach, constructing a QF-list, then the QFP-tree, a quantum radix tree, to efficiently mine frequent itemsets from large datasets. We present a detailed analysis of each step in the QFP-Growth algorithm, providing insights into its time complexity and computational efficiency. Our algorithm outperforms classical FP-Growth with a quadratic improvement in error dependence, showcasing the power of quantum algorithms in data mining. To validate the effectiveness of our approach, we conducted experiments using the IBM QASM simulator, qiskit. The results demonstrate the efficiency and effectiveness of our QFP-Growth algorithm in mining frequent itemsets from a transactional database.en_US
dc.language.isoenen_US
dc.publisherSpringer international Publishing Agen_US
dc.relation.ispartofSymposium on Quantum Sciences, Applications and Challenges (QSAC) -- SEP 24-25, 2023 -- Alger Acad Sci & Tech, Algiers, ALGERIAen_US
dc.relation.ispartofseriesInformation Systems Engineering and Management-
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectQuantum Machine learningen_US
dc.subjectFrequent Itemset Miningen_US
dc.subjectAssociation Rules Miningen_US
dc.subjectFP-growthen_US
dc.subjectIBM QASM Simulatoren_US
dc.subjectQiskiten_US
dc.titleQuantum Fp-Growth for Association Rules Miningen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1007/978-3-031-59318-5_8-
local.message.claim2024-10-23T16:40:49.194+0300|||rp00139|||submit_approve|||dc_contributor_author|||None*
dc.description.woscitationindexConference Proceedings Citation Index - Science-
dc.identifier.wosqualityN/A-
dc.identifier.scopusqualityN/A-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.endpage106en_US
dc.identifier.startpage91en_US
dc.identifier.volume2en_US
dc.departmentMühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.wosWOS:001298001300008en_US
dc.institutionauthorDrias, Yassine-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.02. Department of Computer Engineering-
Appears in Collections:WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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