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arXiv · 2512.00105

Efficiently Sampling Interval Patterns from Numerical Databases

Djawad Bekkoucha·Lamine Diop·Abdelkader Ouali·Bruno Crémilleux·Patrice Boizumault
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Abstract

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.

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Djawad Bekkoucha, Lamine Diop, Abdelkader Ouali, Bruno Crémilleux, Patrice Boizumault. 2025-11-27. Efficiently Sampling Interval Patterns from Numerical Databases. https://arxiv.org/abs/2512.00105

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