Comparison of Apriori Algorithm and Frequent Pattern Growth Approach

Abstract

Data mining is the process of analyzing large data sets in order to find patterns that can be help to isolate key variables to build predictive models for management decision making. The discovery of interesting association relationships among huge amount of business transaction records can help in many business decision making process, such as catalog design, cross marketing and loss leader analysis. Association rule mining is a technique to find useful patterns and associations in transactional databases. Aprirori and Frequent Pattern growth approach are the well-know algorithms for mining frequent item sets in a set of transactions. This system is intended to compare the results (time, number of frequent itemset, Association rules) of the same dataset by applying the Apriori method and Frequent Pattern Growth method. The two dataset, the Kyar Nyo Pan Stationary Store and Orange minimarket are used.