Frequent itemset mining, and association rules

dc.citation.epage203en_US
dc.citation.spage197en_US
dc.contributor.authorImberman, S.en_US
dc.contributor.authorTansel, Abdullah Uzen_US
dc.date.accessioned2018-04-12T13:39:08Z
dc.date.available2018-04-12T13:39:08Z
dc.date.issued2005en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionChapter 26
dc.description.abstractWith the advent of mass storage devices, databases have become larger and larger. Point-of-sale data, patient medical data, scientific data, and credit card transactions are just a few sources of the ever-increasing amounts of data. These large datasets provide a rich source of useful information. Knowledge Discovery in Databases (KDD) is a paradigm for the analysis of these large datasets. KDD uses various methods from such diverse fields as machine learning, artificial intelligence, pattern recognition, database management and design, statistics, expert systems, and data visualization.en_US
dc.identifier.doi10.4018/978-1-59140-573-3.ch026en_US
dc.identifier.isbn9781591405733en_US
dc.identifier.urihttp://hdl.handle.net/11693/37848en_US
dc.language.isoEnglishen_US
dc.publisherIGI Globalen_US
dc.relation.ispartofEncyclopedia of knowledge management
dc.relation.isversionofhttps://doi.org/10.4018/978-1-59140-573-3.ch026en_US
dc.titleFrequent itemset mining, and association rulesen_US
dc.typeBook Chapteren_US

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