DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Mining association rule is one of the key problems in data mining approach. Association rules discover the hidden relationships between various data items. In this paper, we propose a framework for the discovery of association rules using frequent pattern mining. We use preprocessing to transform the transaction dataset into a 2D matrix of 1�s and 0�s. Mining association rule must firstly discover frequent itemsets and then generate strong association rules from the frequent itemsets. The Apriori algorithm is the most well known association rule mining algorithm and is less efficient because they need to scan the database many times and store transaction ID in memory, so time and space overhead is very high. Especially they are less efficient when they process large scale database. Here we propose improved Apriori algorithm by including prune step and hash map data structure. The improved algorithm is more suitable for large scale database. Experimental results shows that computation times are reduced by using the prune step and hash map data structure.
