An Approach to Mine Significant Frequent Patterns by Quantity Attribute

Mining of association rules or frequent patterns has become an important topic in the research of data mining. However the classical Apriori algorithm is used to mine a frequent pattern which is based on Support-Confidence criteria. But it does not mine significant frequent patterns from the transactional database if Quantity, Profit and weight attributes are there. So, this paper introduces a new approach which extracts significant frequent patterns by considering quantity attributes and by applying Q-factor and S-factor to the transactional database. Q-ratio is the ratio of quantity of particular items throughout all transaction to the total quantity of all items of all transaction and S-factor is the product of Q-ratio of particular items and the frequency of particular items throughout all transaction.