Frequent Pattern and Association Rule Mining from Inventory Database Using Apriori Algorithm

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dc.contributor.authorAdewole, K.S.
dc.contributor.authorAkintola, A.G.
dc.contributor.authorAjiboye, A.R.
dc.date.accessioned2018-12-03T13:18:25Z
dc.date.available2018-12-03T13:18:25Z
dc.date.issued2014-09
dc.descriptionMain articleen_US
dc.description.abstractRecently, data mining has attracted a great deal of attention in the information industry and in a Society where data continue to grow on a daily basis. The availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge is the major focus of data mining. The information and knowledge obtained from large data can be used for applications ranging from market analysis, fraud detection, production control, customer retention, and science exploration. A record in such data typically consists of the transaction date and the items bought in the transaction. Successful organizations view such databases as important pieces of the marketing infrastructure. This paper considers the problem of mining association rules between items in a large database of sales transactions in order to understand customer-buying habits for the purpose of improving sales. Apriori algorithm was used for generating strong rules from inventory database. It was found that for a transactional database where many transaction items are repeated many times as a superset in that type of database, Apriori is suited for mining frequent itemsets. The algorithm was implemented using PHP, and MySQL database management system was used for storing the inventory data. The algorithm produces frequent itemsets completely and generates the accurate strong rules.en_US
dc.identifier.issn2006-1781
dc.identifier.urihttp://hdl.handle.net/123456789/1327
dc.language.isoenen_US
dc.publisherAfrican Journal of Computing & ICTen_US
dc.relation.ispartofseries;Vol 7. No. 3
dc.subjectApriori Algorithmen_US
dc.subjectdata miningen_US
dc.subjectdatabaseen_US
dc.subjectstrong rulesen_US
dc.subjectinventoryen_US
dc.titleFrequent Pattern and Association Rule Mining from Inventory Database Using Apriori Algorithmen_US
dc.typeArticleen_US

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