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dc.contributor.authorWakabi-Waiswa, Peter P.
dc.contributor.authorBaryamureeba, Venansius
dc.contributor.authorSarukesi, K.
dc.date.accessioned2022-07-17T15:47:30Z
dc.date.available2022-07-17T15:47:30Z
dc.date.issued2008
dc.identifier.citationWakabi-Waiswa, P. P., Baryamureeba, V., & Sarukesi, K. (2008). Generalized Association Rule Mining Using Genetic Algorithms. Strengthening the Role of ICT in Development, 59. ISBN 978-9970-02-871-2en_US
dc.identifier.isbn978-9970-02-871-2
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4214
dc.description.abstractWe formulate a general Association rule mining model for extracting useful information from very large databases. An interactive Association rule mining system is designed using a combination of genetic algorithms and a modified a-priori based algorithm. The association rule mining problem is modeled as a multi-objective combinatorial problem which is solved using genetic algorithms. The combination of genetic algorithms with a-priori query optimization make association rule mining yield fast results. In this paper we use the same combination to extend it to a much more general context allowing efficient mining of very large databases for many different kinds of patterns. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. We show how the idea can be used either in a general purpose mining system or in a next generation of conventional query optimizers.en_US
dc.language.isoenen_US
dc.publisherFountain Publishersen_US
dc.subjectRule Miningen_US
dc.subjectGenetic Algorithmsen_US
dc.titleGeneralized Association Rule Mining Using Genetic Algorithmsen_US
dc.typeBook chapteren_US


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