# HUOPM: High Utility Occupancy Pattern Mining

arXiv: https://arxiv.org/abs/1812.10926 · HTML: https://mlsanity.com/paper/1812.10926/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2018.

## Built on by (most cited first, 10 of 10)

- [Large Language Models in Education: Vision and Opportunities](https://mlsanity.com/paper/2311.13160/) (2023)
- [TargetUM: Targeted High-Utility Itemset Querying](https://mlsanity.com/paper/2111.00309/) (2021)
- [OPP-Miner: Order-preserving sequential pattern mining](https://mlsanity.com/paper/2202.03140/) (2022)
- [Flexible Pattern Discovery and Analysis](https://mlsanity.com/paper/2111.12218/) (2021)
- [MDL-based Compressing Sequential Rules](https://mlsanity.com/paper/2212.10252/) (2022)
- [Artificial Intelligence in Landscape Architecture: A Survey](https://mlsanity.com/paper/2408.14700/) (2024)
- [Smart System: Joint Utility and Frequency for Pattern Classification](https://mlsanity.com/paper/2206.04269/) (2022)
- [Towards Target High-Utility Itemsets](https://mlsanity.com/paper/2206.06157/) (2022)
- [A Generic Algorithm for Top-K On-Shelf Utility Mining](https://mlsanity.com/paper/2208.14230/) (2022)
- [Towards Sequence Utility Maximization under Utility Occupancy Measure](https://mlsanity.com/paper/2212.10452/) (2022)

## Abstract (arXiv)

Mining useful patterns from varied types of databases is an important research topic, which has many real-life applications. Most studies have considered the frequency as sole interestingness measure for identifying high quality patterns. However, each object is different in nature. The relative importance of objects is not equal, in terms of criteria such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, i.e., they often represent small sets of items in transactions containing many items, and thus may not be truly representative of these transactions. To extract high quality patterns in real life applications, this paper extends the occupancy measure to also assess the utility of patterns in transaction databases. We propose an efficient algorithm named High Utility Occupancy Pattern Mining (HUOPM). It considers user preferences in terms of frequency, utility, and occupancy. A novel Frequency-Utility tree (FU-tree) and two compact data structures, called the utility-occupancy list and FU-table, are designed to provide global and partial downward closure properties for pruning the search space. The proposed method can efficiently discover the complete set of high quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of the proposed algorithm. Results show that the derived patterns are intelligible, reasonable and acceptable, and that HUOPM with its pruning strategies outperforms the state-of-the-art algorithm, in terms of runtime and search space, respectively.