Itemset Utility Maximization with Correlation Measure

TL;DR

This paper introduces CoIUM, an algorithm for high utility itemset mining that incorporates item correlation, improving efficiency by pruning and database projection, and outperforms existing methods in speed and memory use.

Contribution

The paper presents a novel algorithm, CoIUM, which considers both correlation and utility, employing new pruning strategies and data structures for efficient high utility itemset mining.

Findings

CoIUM significantly reduces runtime compared to state-of-the-art algorithms.

The algorithm effectively decreases memory consumption during mining.

Experimental results validate CoIUM's superior performance on various datasets.

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.