High-utility itemset mining (HUIM) is a useful tool for analyzing customer behavior in the field of data mining. HUIM algorithms can discover the most beneficial itemsets in transaction databases, namely the high-util...
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High-utility itemset mining (HUIM) is a useful tool for analyzing customer behavior in the field of data mining. HUIM algorithms can discover the most beneficial itemsets in transaction databases, namely the high-utility itemsets (HUIs), in contrast to frequent itemset mining (FIM) algorithms that rely on detecting frequent patterns. Several algorithms have been proposed to effectively carry out this task, but most of them ignore the categorization of items. In many real-world transaction databases, this helpful information about the categories and subcategories of items, represented as a taxonomy, is useful. Therefore, traditional HUIM algorithms can only discover itemsets at the lowest level of abstraction and leave out several important patterns from higher levels. To address this limitation, this work suggests the use of items taxonomy. Besides, to further enhance the performance of the task several effective pruning techniques are also revised and utilized to tighten the search space when considering the taxonomy of items. To accurately find multi-level HUIs from transaction databases enhanced with taxonomy information, a new algorithm called MLHMiner (multiple-level HMiner) is proposed, which is an extended version of the HMiner algorithm. We also prove that the pruning techniques of HMiner can be applied in different abstraction levels to efficiently mine multi-level HUIs. It can be seen from the experimental evaluations on several databases (both real and synthetic) that the designed approach is capable of identifying useful patterns from different abstraction levels with high efficiency.
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. ...
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ISBN:
(纸本)9783030732790;9783030732806
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 traditionalHUIMapproaches. 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 theMCML-Miner up to several folds faster than the original algorithm.
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