Abstract:
The association rule mining is one of the primary sub-areas in the field of data mining . This type of mining ,the association rule searches for interesting relation among item in a given data set , has been used in numerous practical applications : market basket analysis , catalog design and loss-leader analysis . In this paper, Apriori algorithm is implied for mining frequent itemsets for Boolean association rules with a large amount of items in a database. Consequently, every itemset is employed with level-wise search to provide achieving more frequent itemsets found. Furthermore , the user is eventually allowed to understand this system with two step process including join and prune actions. As a result, the valuable information is allowed to the user with a variety of threshold value. The association rule resulted from Apriori algorithm is also improved into strong rule according to these threshold values.
