THE METHOD FOR HIGH UTILITY PATTERN MINING OVER TRANSACTIONAL DATA STREAM BASED ON HUSTREE

Abstract

High utility pattern mining over transactional data streams is an important research issue in the field of data mining. The mining approach is used to discover high utility itemsets in transactional databases. Furthermore, the constantly changing number of transactions in data streams generates new high utility patterns and modifies the utility of previously discovered patterns. Timely updating of this changing information is crucial for making effective business decisions. However, the number of available mining methods for transactional data streams is still limited. In this paper, we propose a new method for mining transactional data streams using a HUS-Tree. The experimental results show that our new method is more efficient in terms of execution time than previous solutions.