  There are large amounts of transactional data which showed consumer shopping
cart at a store that sells more than 150 types of products. In this case, the
company is utilizing these data in making business action. In previous studies,
the data that has a lot of attributes and record data reduction algorithms
handled by the FSA Red (Feature Selection for Association Rules)are then mined
using Apriori algorithm. The resulting association rules have high levels of
accuracy and excellent test results, which rely more than 90%. In this study,
the association rules generated in previous research will be updated by using
prediction algorithms M5P, so that the association rules can be used within a
period of several months in the future. Furthermore, some data mining technique
such as: clustering and time series pattern will be implemented to examine the
truth and extend the validity of association rules which were built. It can be
concluded that the association rules were established after will generate
strong association rules with confidence equal or higher than 70% and the rules
established truth can be seen from the time series pattern on each group of
goods which are then used as the basis of business actions.
