Frequent Pattern and Association Rule Mining from Inventory Database Using Apriori Algorithm
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Date
2014-09
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African Journal of Computing & ICT
Abstract
Recently, 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.
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Keywords
Apriori Algorithm, data mining, database, strong rules, inventory