Apriori Algorithm for Vertical Association Rule Mining

Association rule mining is one of the important concepts in data mining domain for analyzing customer’s data. The association rule mining is a process of finding correlation among the items involved in different transactions. Traditionally association rule mining is implemented horizontally. For this we have plenty of different algorithms in research like Apriori based, FP tree based so on. Recently we have a new method in association rule mining which generates vertical association rules. In horizontal association rule mining we read transaction items record by record basis and computes support of each frequent item or candidate item. We repeat this process to generate frequent item sets. The vertical association rule mining evaluates support frequency of each item column wise. For this it uses bitmap matrix that saves support sets of frequent item sets in memory which is used to calculate candidate item sets. The Item are read from Data set using BitMap Matrix format which uses 1 or 0 to represent the presence or absence of item in record.