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dc.contributor.authorNakibinge, Gideon
dc.date.accessioned2021-04-22T07:16:06Z
dc.date.available2021-04-22T07:16:06Z
dc.date.issued2021-02
dc.identifier.urihttp://hdl.handle.net/20.500.12281/10278
dc.descriptionA Dissertation Submitted to the School of Statistics and Planning In Partial Fulfillment for the Degree of Bachelor of Statistics at Makerere Universityen_US
dc.description.abstractMarket basket analysis (also known as association-rule mining) in modern retailing has been a useful method in discovering customer purchasing patterns by extracting associations or co-occurrences from stores’ transaction databases which are readily available recently because of the growth in technology. This information obtained from the analysis can be used in forming marketing, sales, service, and operation strategies. The existing methods, however, fail to discover important purchasing patterns in a multi-store environment, because of an implicit assumption that products under consideration are on shelf all the time. In this study, i propose a new method to overcome this weakness. My empirical evaluation shows that the proposed recommendation system is computationally efficient, and it has advantage over the traditional method when stores are diverse in size, product mix changes rapidly over time, and larger numbers of stores and periods are considered.en_US
dc.language.isoenen_US
dc.publisherMakerere Universityen_US
dc.subjectMarket basketen_US
dc.subjectCustomer purchasing patternsen_US
dc.subjectStores’ transaction databasesen_US
dc.titleRetail recommendation systemen_US
dc.typeThesisen_US


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