# Targeted Mining of Time-Interval Related Patterns

arXiv: https://arxiv.org/abs/2507.12668 · HTML: https://mlsanity.com/paper/2507.12668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Enhancing Temporal Awareness in LLMs for Temporal Point Processes](https://mlsanity.com/paper/2601.00845/) (2025)

## Abstract (arXiv)

Compared to frequent pattern mining, sequential pattern mining emphasizes the temporal aspect and finds broad applications across various fields. However, numerous studies treat temporal events as single time points, neglecting their durations. Time-interval-related pattern (TIRP) mining is introduced to address this issue and has been applied to healthcare analytics, stock prediction, etc. Typically, mining all patterns is not only computationally challenging for accurate forecasting but also resource-intensive in terms of time and memory. Targeting the extraction of time-interval-related patterns based on specific criteria can improve data analysis efficiency and better align with customer preferences. Therefore, this paper proposes a novel algorithm called TaTIRP to discover Targeted Time-Interval Related Patterns. Additionally, we develop multiple pruning strategies to eliminate redundant extension operations, thereby enhancing performance on large-scale datasets. Finally, we conduct experiments on various real-world and synthetic datasets to validate the accuracy and efficiency of the proposed algorithm.