# TaSPM: Targeted Sequential Pattern Mining

arXiv: https://arxiv.org/abs/2202.13202 · HTML: https://mlsanity.com/paper/2202.13202/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 26 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [A Survey of Parallel Sequential Pattern Mining](https://mlsanity.com/paper/1805.10515/) (2018)
- [ProUM: Projection-based Utility Mining on Sequence Data](https://mlsanity.com/paper/1904.07764/) (2019)
- [TUSQ: Targeted High-Utility Sequence Querying](https://mlsanity.com/paper/2103.16615/) (2021)
- [Explainable Fuzzy Utility Mining on Sequences](https://mlsanity.com/paper/2103.16519/) (2021)
- [Skopus: Mining top-k sequential patterns under leverage](https://mlsanity.com/paper/1506.08009/) (2015)
- [Utility Mining Across Multi-Dimensional Sequences](https://mlsanity.com/paper/1902.09582/) (2019)
- [TargetUM: Targeted High-Utility Itemset Querying](https://mlsanity.com/paper/2111.00309/) (2021)

## Built on by (most cited first, 2 of 2)

- [Towards Target Sequential Rules](https://mlsanity.com/paper/2206.04728/) (2022)
- [Towards Target High-Utility Itemsets](https://mlsanity.com/paper/2206.06157/) (2022)

## Abstract (arXiv)

Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies can focus on target sequences. Targeted querying sequential patterns can not only reduce the number of sequences generated by SPM, but also improve the efficiency of users in performing pattern analysis. The current algorithms available on targeted sequence querying are based on specific scenarios and cannot be generalized to other applications. In this paper, we formulate the problem of targeted sequential pattern mining and propose a generic framework namely TaSPM, based on the fast CM-SPAM algorithm. What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes. Totally four pruning strategies are designed in TaSPM, and hence it can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conduct extensive experiments on different datasets to compare the existing SPM algorithms with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.