An Efficient Approach for Mining High-Utility Itemsets from Multiple Abstraction Levels

The goal of the high-utility itemset mining task is to discover combinations of items which that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, it ignores the categorization of items. To solve this issue, the ML-HUI Miner algorithm was presented. It combines item taxonomy with the HUIM task and is able to discover insightful itemsets, which are not found in traditional HUIM approaches. Although ML-HUI Miner is efficient in discovering itemsets from multiple abstraction levels, it is a sequential algorithm. Thus, it cannot utilize the powerful multi-core processors, which are currently available widely. This paper addresses this issue by extending the algorithm into a multi-core version, called the MCML-Miner algorithm (Multi-Core Multi-Level high-utility itemset Miner), to help reduce significantly the mining time. Each level in the taxonomy will be assigned a separate processor core to explore concurrently. Experiments on real-world datasets show that the MCML-Miner up to several folds faster than the original algorithm.