# Itemset Utility Maximization with Correlation Measure

arXiv: https://arxiv.org/abs/2208.12551 · HTML: https://mlsanity.com/paper/2208.12551/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [A Survey of Parallel Sequential Pattern Mining](https://mlsanity.com/paper/1805.10515/) (2018)
- [Correlated Utility-based Pattern Mining](https://mlsanity.com/paper/1904.03336/) (2019)
- [TargetUM: Targeted High-Utility Itemset Querying](https://mlsanity.com/paper/2111.00309/) (2021)
- [FUIM: Fuzzy Utility Itemset Mining](https://mlsanity.com/paper/2111.00307/) (2021)

## Abstract (arXiv)

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.