Discovery of High Utility Itemsets Using Genetic Algorithm with Ranked Mutation

Abstract

Utility mining is the study of itemset mining from the consideration of utilities. It is the utility-based itemset mining approach to find itemsets conforming to user preferences. Modern research in mining high-utility itemsets (HUI) from the databases faces two major challenges: exponential search space and database-dependent minimum utility threshold. The search space is extremely vast when the number of distinct items and the size of the database are very large. Data analysts must specify suitable minimum utility thresholds for their mining tasks, although they might have no knowledge pertaining to their databases. Moreover, a utility-mining algorithm supports only an itemset with positive item values. To evade these problems, two approaches are presented for mining HUI containing negative item values from transaction databases: with/without specifying the minimum utility threshold through a genetic algorithm with ranked mutation. To the best of our knowledge, this is the first work on mining HUI with negative item values from transaction databases using a genetic algorithm. Experimental results show that approaches described in this article achieve better performance in terms of scalability and efficiency.