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dc.contributor.authorOğuz, Damla
dc.contributor.authorErgenç, Belgin
dc.date.accessioned2019-11-11T13:21:19Z
dc.date.available2019-11-11T13:21:19Z
dc.date.issued2012en_US
dc.identifier.isbn978-364232583-0
dc.identifier.urihttps://doi.org/10.1007/978-3-642-32584-7_16
dc.identifier.urihttps://hdl.handle.net/11147/7345
dc.description14th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2012; Vienna; Austria; 3 September 2012 through 6 September 2012en_US
dc.description.abstractDatabases are updated continuously with increments and re-running the frequent itemset mining algorithms with every update is inefficient. Studies addressing incremental update problem generally propose incremental itemset mining methods based on Apriori and FP-Growth algorithms. Besides inheriting the disadvantages of base algorithms, incremental itemset mining has challenges such as handling i) increments without re-running the algorithm, ii) support changes, iii) new items and iv) addition/deletions in increments. In this paper, we focus on the solution of incremental update problem by proposing the Incremental Matrix Apriori Algorithm. It scans only new transactions, allows the change of minimum support and handles new items in the increments. The base algorithm Matrix Apriori works without candidate generation, scans database only twice and brings additional advantages. Performance studies show that Incremental Matrix Apriori provides speed-up between 41% and 92% while increment size is varied between 5% and 100%.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/978-3-642-32584-7_16en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIncremental itemset miningen_US
dc.subjectMatrix Apriorien_US
dc.subjectLearning algorithmsen_US
dc.titleIncremental itemset mining based on matrix Apriori algorithmen_US
dc.typeconferenceObjecten_US
dc.contributor.authorID0000-0001-6193-9853en_US
dc.contributor.iztechauthorOğuz, Damla
dc.contributor.iztechauthorErgenç, Belgin
dc.relation.journal14th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2012en_US
dc.contributor.departmentIzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.volume7448 LNCSen_US
dc.identifier.startpage192en_US
dc.identifier.endpage204en_US
dc.identifier.scopusSCOPUS:2-s2.0-84866665272
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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