Apriori Algorithm for Vertical Association Rule Mining
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 3, Issue 8
Abstract
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. In our system it is proposed to combine both horizontal mining and vertical mining in generating association rules. The horizontal and vertical mining are implemented in parallel using multithreading concept. For this we propose a modified parallel multithreaded Apriori algorithm. The algorithm saves time and decreases memory space as the process is running because of bitmap representation of dataset and bitmap compression algorithms.
Authors and Affiliations
Mohammed Karimuddin
Keywords
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