Itemset Utility Maximization with Correlation Measure

• URL: http://arxiv.org/abs/2208.12551v1
• Date: Fri, 26 Aug 2022 10:06:24 GMT
• Title: Itemset Utility Maximization with Correlation Measure
• Authors: Jiahui Chen, Yixin Xu, Shicheng Wan, Wensheng Gan, and Jerry Chun-Wei Lin
• Abstract summary: High utility itemset mining (HUIM) is used to find out interesting but hidden information (e.g., profit and risk) In this paper, we propose a novel algorithm called the Itemset Utility Maximization with Correlation Measure (CoIUM) Two upper bounds and four pruning strategies are utilized to effectively prune the search space. And a concise array-based structure named utility-bin is used to calculate and store the adopted upper bounds in linear time and space.
• Score: 8.581840054840335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an important data mining technology, high utility itemset mining (HUIM) is used to find out interesting but hidden information (e.g., profit and risk). HUIM has been widely applied in many application scenarios, such as market analysis, medical detection, and web click stream analysis. However, most previous HUIM approaches often ignore the relationship between items in an itemset. Therefore, many irrelevant combinations (e.g., \{gold, apple\} and \{notebook, book\}) are discovered in HUIM. To address this limitation, many algorithms have been proposed to mine correlated high utility itemsets (CoHUIs). In this paper, we propose a novel algorithm called the Itemset Utility Maximization with Correlation Measure (CoIUM), which considers both a strong correlation and the profitable values of the items. Besides, the novel algorithm adopts a database projection mechanism to reduce the cost of database scanning. Moreover, two upper bounds and four pruning strategies are utilized to effectively prune the search space. And a concise array-based structure named utility-bin is used to calculate and store the adopted upper bounds in linear time and space. Finally, extensive experimental results on dense and sparse datasets demonstrate that CoIUM significantly outperforms the state-of-the-art algorithms in terms of runtime and memory consumption.

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