TargetUM: Targeted High-Utility Itemset Querying

TL;DR

TargetUM introduces a novel targeted high-utility itemset querying method that efficiently retrieves user-specified itemsets using a tree-based algorithm with pruning strategies, reducing database scans.

Contribution

It formulates the targeted utility mining problem and proposes the first tree-based algorithm, TargetUM, for efficient, user-focused high-utility itemset querying.

Findings

TargetUM outperforms existing methods in efficiency and accuracy.

The algorithm reduces repeated database scans for multiple queries.

Experimental results validate the effectiveness on real and synthetic data.

Abstract

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (\textit{minUtil}) in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of \textbf{TargetUM} is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.