Mining High Utility Itemsets from Large Transactions using Efficient Tree Structure

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. It is an extension of the frequent pattern mining. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. Association rule mining with item set frequencies are used to extract item set relationships. Frequent pattern mining algorithms are designed to find commonly occurring sets in databases. Memory and run time requirements are very high in frequent pattern mining algorithms. Systolic tree structure is a reconfigurable architecture used for utility pattern mining operations. High throughput and faster execution are the highlights of the systolic tree based reconfigurable architecture. The systolic tree mechanism is used in the frequent pattern extraction process for the transaction database. Systolic tree based rule mining scheme is enhanced for Weighted Association Rule Mining (WARM) process, used to fetch the frequently accessed transaction database with its weight values. The dynamic dataset is weight assignment scheme uses the item request count and span time values. The proposed system improves the weight estimation process with span time, request count and access sequence details.