# TargetUM: Targeted High-Utility Itemset Querying

arXiv: https://arxiv.org/abs/2111.00309 · HTML: https://mlsanity.com/paper/2111.00309/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 30 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [ProUM: Projection-based Utility Mining on Sequence Data](https://mlsanity.com/paper/1904.07764/) (2019)
- [HUOPM: High Utility Occupancy Pattern Mining](https://mlsanity.com/paper/1812.10926/) (2018)
- [TUSQ: Targeted High-Utility Sequence Querying](https://mlsanity.com/paper/2103.16615/) (2021)
- [TKUS: Mining Top-K High-Utility Sequential Patterns](https://mlsanity.com/paper/2011.13454/) (2020)
- [Privacy Preserving Utility Mining: A Survey](https://mlsanity.com/paper/1811.07389/) (2018)
- [A Guided FP-growth algorithm for multitude-targeted mining of big data](https://mlsanity.com/paper/1803.06632/) (2018)

## Built on by (most cited first, 4 of 4)

- [TaSPM: Targeted Sequential Pattern Mining](https://mlsanity.com/paper/2202.13202/) (2022)
- [Towards Target Sequential Rules](https://mlsanity.com/paper/2206.04728/) (2022)
- [Towards Target High-Utility Itemsets](https://mlsanity.com/paper/2206.06157/) (2022)
- [Itemset Utility Maximization with Correlation Measure](https://mlsanity.com/paper/2208.12551/) (2022)

## Abstract (arXiv)

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (textitminUtil) 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 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.