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.
