# TKUS: Mining Top-K High-Utility Sequential Patterns

arXiv: https://arxiv.org/abs/2011.13454 · HTML: https://mlsanity.com/paper/2011.13454/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [US-Rule: Discovering Utility-driven Sequential Rules](https://mlsanity.com/paper/2111.15020/) (2021)
- [Anomaly Rule Detection in Sequence Data](https://mlsanity.com/paper/2111.15026/) (2021)
- [TargetUM: Targeted High-Utility Itemset Querying](https://mlsanity.com/paper/2111.00309/) (2021)
- [Totally-ordered Sequential Rules for Utility Maximization](https://mlsanity.com/paper/2209.13501/) (2022)
- [Smart System: Joint Utility and Frequency for Pattern Classification](https://mlsanity.com/paper/2206.04269/) (2022)
- [A Generic Algorithm for Top-K On-Shelf Utility Mining](https://mlsanity.com/paper/2208.14230/) (2022)
- [Towards Correlated Sequential Rules](https://mlsanity.com/paper/2210.15637/) (2022)
- [HUSP-SP: Faster Utility Mining on Sequence Data](https://mlsanity.com/paper/2212.14255/) (2022)
- [A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences](https://mlsanity.com/paper/2410.07282/) (2024)

## Abstract (arXiv)

High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top-k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top-k HUSPs, and existing strategies require improvement in terms of running time, memory consumption, unpromising candidate filtering, and scalability. Moreover, no systematic problem statement has been defined. In this paper, we formulate the problem of top-k HUSPM and propose a novel algorithm called TKUS. To improve efficiency, TKUS adopts a projection and local search mechanism and employs several schemes, including the Sequence Utility Raising, Terminate Descendants Early, and Eliminate Unpromising Items strategies, which allow it to greatly reduce the search space. Finally, experimental results demonstrate that TKUS can achieve sufficiently good top-k HUSPM performance compared to state-of-the-art algorithm TKHUS-Span.