Efficient High Utility Itemset Mining Using Genetic Algorithms and Bit-Vector Optimization
摘要
The paper focuses on High-Utility Itemset Mining (HUIM). The algorithms to mine high utility itemsets face exponential search time due to the growing number of transactions. An algorithm that employs genetic approach has been proposed in this paper to address this challenge. It utilizes techniques of genetic approach to avoid unfit individuals. Transaction clustering is also applied to the database, reducing the time required for database scanning during itemset utility calculations.