# Smart System: Joint Utility and Frequency for Pattern Classification

arXiv: https://arxiv.org/abs/2206.04269 · HTML: https://mlsanity.com/paper/2206.04269/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [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)
- [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)
- [On-shelf Utility Mining of Sequence Data](https://mlsanity.com/paper/2011.13455/) (2020)
- [Discovering High Utility-Occupancy Patterns from Uncertain Data](https://mlsanity.com/paper/2008.08190/) (2020)
- [Utility-driven Data Analytics on Uncertain Data](https://mlsanity.com/paper/1902.09586/) (2019)
- [Utility-Driven Mining of Trend Information for Intelligent System](https://mlsanity.com/paper/1912.11666/) (2019)

## Abstract (arXiv)

Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things (IoT) are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized and thus help manufacturing organizations to finish another round of upgrading. In this paper, we propose two new algorithms with respect to big data analysis, namely UFCgen and UFCfast. Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFCfast algorithm outperforms the level-wise-based UFCgen algorithm in terms of both execution time and memory consumption.