# Efficiently Sampling Interval Patterns from Numerical Databases

arXiv: https://arxiv.org/abs/2512.00105 · HTML: https://mlsanity.com/paper/2512.00105/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [Flexible constrained sampling with guarantees for pattern mining](https://mlsanity.com/paper/1610.09263/) (2016)
- [Learning what matters - Sampling interesting patterns](https://mlsanity.com/paper/1702.01975/) (2017)
- [Scalable Sampling for High Utility Patterns](https://mlsanity.com/paper/2410.22964/) (2024)

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

- [Frequency-based Constrained Sampling for Interval Patterns](https://mlsanity.com/paper/2606.09666/) (2026)

## Abstract (arXiv)

Pattern sampling has emerged as a promising approach for information discovery in large databases, allowing analysts to focus on a manageable subset of patterns. In this approach, patterns are randomly drawn based on an interestingness measure, such as frequency or hyper-volume. This paper presents the first sampling approach designed to handle interval patterns in numerical databases. This approach, named Fips, samples interval patterns proportionally to their frequency. It uses a multi-step sampling procedure and addresses a key challenge in numerical data: accurately determining the number of interval patterns that cover each object. We extend this work with HFips, which samples interval patterns proportionally to both their frequency and hyper-volume. These methods efficiently tackle the well-known long-tail phenomenon in pattern sampling. We formally prove that Fips and HFips sample interval patterns in proportion to their frequency and the product of hyper-volume and frequency, respectively. Through experiments on several databases, we demonstrate the quality of the obtained patterns and their robustness against the long-tail phenomenon.