Research of Improved Association Rule Algorithms (Apriori and FP-Growth)

Abstract: - Association rule mining explores interesting relationships among items in a given data set. An objective of association rule mining is to develop a systematic method using the given database and finds relationships between the different items. Goal of association rules finding associations among items from a set of transactions, which contain a set of items. In this paper we focused on explaining the fundamentals of association mining and analyze implementations of the well-known association rule algorithms. Study focuses on algorithms Apriori, FP-Growth, and Dynamic Itemset Counting. Moreover, the algorithm generates frequent item sets in order so that the result can be used expediently.

Key Terms – Association Rule Mining, FP-Growth Algorithm, Apriori algorithm, Frequent Itemsets.