# HUOPM: High Utility Occupancy Pattern Mining

## TL;DR

This paper introduces HUOPM, an efficient algorithm for mining high utility occupancy patterns that incorporate utility, frequency, and occupancy to find more representative and meaningful patterns in transaction databases.

## Contribution

It extends traditional pattern mining by integrating occupancy with utility and frequency, proposing novel data structures and pruning strategies for improved efficiency.

## Key findings

- HUOPM outperforms existing algorithms in runtime.
- The patterns discovered are more representative and meaningful.
- The method effectively balances utility, frequency, and occupancy.

## Abstract

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.