Comparison of Apriori Algorithm and Frequent Pattern Growth Approach

Data mining is the process of analyzinglarge data sets in order to find patterns that can behelp to isolate key variables to build predictivemodels for management decision making. Thediscovery of interesting association relationshipsamong huge amount of business transaction recordscan help in many business decision making process,such as catalog design, cross marketing and lossleader analysis. Association rule mining is atechnique to find useful patterns and associations intransactional databases. Aprirori and FrequentPattern growth approach are the well-knowalgorithms for mining frequent item sets in a set oftransactions. This system is intended to compare theresults (time, number of frequent itemset, Associationrules) of the same dataset by applying the Apriorimethod and Frequent Pattern Growth method. Thetwo dataset, the Kyar Nyo Pan Stationary Store andOrange minimarket are used