Evaluation of Apriori Algorithm on Retail Market Transactional Database to get Frequent Itemsets

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Abstract

In Data mining the concept of association rule mining (ARM) is used to identify the frequent itemsets from large datasets. It defines frequent pattern mining from larger datasets using Apriori algorithm & FP-growth algorithm. The Apriori algorithm is a classic traditional algorithm for the mining all frequent itemsets and association rules. But, the traditional Apriori algorithm have some limitations i.e. there are more candidate sets generation & huge memory consumption, etc. Still, there is a scope for improvement to modify the existing Apriori algorithm for identifying frequent itemsets with a focus on reducing the computational time and memory space. This paper presents the analysis of existing Apriori algorithms and results of the traditional Apriori algorithm. Experimentation carried out on transactional database i.e. retail market for getting frequent itemsets. The traditional Apriori algorithm is evaluated in terms of support and confidence of transactional itemsets.

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