Discovery of frequent itemsets based on minimum quantity andsupport
Preetham Kumar, V. S. Ananthanarayana · 2009
Most of the association rules mining algorithms to discover frequent itemsets do not consider the components of transactions such as total cost, number of items or quantity in which items bought. In a large database it is possible that even if the itemset appears in a very few transactions, it may be purchased in a large quantity for every transaction in which it is present, and may lead to a very high profit. Therefore the quantity and the total cost of the item bought are the most important components, lack of which may lead to loss of information. Our novelmethod discovers all frequent itemsets based on items quantity, in addition to the discovery of frequent itemsets based on user defined minimum support. In order to achieve this, we first construct a tree containing the quantities of the items bought, as well as the transactions which do not contain these items in a single scan of the database. Then, by scanning the tree, we can discover all frequent itemsets based on user defined minimum quantity as well as support. Thismethod is also found to be more efficient than the Apriori and the FP-tree, whichrequire multiple scans of the database to discover all frequent itemsets based on user defined minimum support.