A study of selected itemset mining and association rule mining algorithms

A tétel rövid nézete
dc.contributor.advisorSzathmáry, László
dc.contributor.authorHammami, Rim
dc.contributor.departmentDE--Informatikai Karhu_HU
dc.date.accessioned2022-04-30T22:42:20Z
dc.date.available2022-04-30T22:42:20Z
dc.date.created2022-04-29
dc.description.abstractData Mining is a central step in Knowledge Discovery in Databases (KDD). This thesis is within the context of Data Mining and more specifically revolves around a couple the Data Mining tasks: Itemset Mining and Association Rule Mining. In this thesis, the aim was to investigate a selection of Itemset Mining algorithms as well as Association Rule Mining. For Frequent Itemset Mining, I studied Apriori, its variant Apriori-Close, and Eclat. I also brought attention to Rare Itemset Mining and investigated Apriori-Rare. I discussed the simple version of Association Rule Mining and tested it using both Apriori and Eclat. Lastly, I talked a about the software I developed in order to test these algorithms.hu_HU
dc.description.courseComputer Sciencehu_HU
dc.description.degreeMSc/MAhu_HU
dc.format.extent65hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/331972
dc.language.isoenhu_HU
dc.subjectSymbolic Data Mininghu_HU
dc.subjectAssociation Rule Mininghu_HU
dc.subjectFrequent Itemset Mininghu_HU
dc.subject.dspaceDEENK Témalista::Informatikahu_HU
dc.titleA study of selected itemset mining and association rule mining algorithmshu_HU
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