Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/3785
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dc.contributor.advisorPüskülcü, Halisen
dc.contributor.authorÖzsevim, Emrah-
dc.date.accessioned2014-07-22T13:52:21Z-
dc.date.available2014-07-22T13:52:21Z-
dc.date.issued2003en
dc.identifier.urihttp://hdl.handle.net/11147/3785-
dc.descriptionThesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2003en
dc.descriptionIncludes bibliographical references (leaves: 92-97)en
dc.descriptionText in English; Abstract: Turkish and Englishen
dc.description97 leavesen
dc.description.abstractThe growth of large-scale transactional databases, time-series databases and other kinds of databases has been giving rise to the development of several efficient algorithms that cope with the computationally expensive task of association rule mining.In this study, different algorithms, Apriori, FP-tree and CHARM, for exploiting the hidden trends such as frequent itemsets, frequent patterns, closed frequent itemsets respectively, were discussed and their performances were evaluated. The perfomances of the algorithms were measured at different support levels, and the algorithms were tested on different data sets (on both synthetic and real data sets). The algorihms were compared according to their, data preparation performances, mining performance, run time performances and knowledge extraction capabilities.The Apriori algorithm is the most prevalent algorithm of association rule mining which makes multiple passes over the database aiming at finding the set of frequent itemsets for each level. The FP-Tree algorithm is a scalable algorithm which finds the crucial information as regards the complete set of prefix paths, conditional pattern bases and frequent patterns by using a compact FP-Tree based mining method. The CHARM is a novel algorithm which brings remarkable improvements over existing association rule mining algorithms by proving the fact that mining the set of closed frequent itemsets is adequate instead of mining the set of all frequent itemsets.Related to our experimental results, we conclude that the Apriori algorithm demonstrates a good performance on sparse data sets. The Fp-tree algorithm extracts less association in comparison to Apriori, however it is completelty a feasable solution that facilitates mining dense data sets at low support levels. On the other hand, the CHARM algorithm is an appropriate algorithm for mining closed frequent itemsets (a substantial portion of frequent itemsets) on both sparse and dense data sets even at low levels of support.en
dc.language.isoenen_US
dc.publisherIzmir Institute of Technologyen
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.lccQA76.9.D343 O97 2003en
dc.subject.lcshData miningen
dc.subject.lcshAssociation rule miningen
dc.subject.lcshAlgorithmsen
dc.titleComparison of different algorithms for exploting the hidden trends in data sourcesen_US
dc.typeMaster Thesisen_US
dc.institutionauthorÖzsevim, Emrah-
dc.departmentThesis (Master)--İzmir Institute of Technology, Computer Engineeringen_US
dc.relation.publicationcategoryTezen_US
item.fulltextWith Fulltext-
item.openairetypeMaster Thesis-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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