Performance Analysis of Apriori and FP-Growth Algorithms on Online-Store Data

Mining frequent item sets from transaction datasets is always being one of the most important problems of data mining. Apriori is the most popular and simplest algorithm for frequent itemset mining. Transaction data is a set of recording data resulting in connections with sales-purchase activities at a particular online store. In recent years transaction data has been used in research for discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. This paper focuses on the performance comparison of Apriori and FP-growth algorithms with respect to the execution time.