提出高效采样数值数据库中区间模式的新方法，解决长尾问题。
Efficiently Sampling Interval Patterns from Numerical Databases
- 基于频率比例多步采样，精准统计覆盖对象的区间模式数。
- Fips按频率采样，HFips结合频率与超体积双重加权，提升代表性。
- 适合数据挖掘、异常检测等需高效发现关键模式的场景。
模式采样已成为大型数据库中信息发现的有力手段，使分析者能聚焦于可管理的模式子集。本文首次提出针对数值数据库中区间模式的采样方法Fips，该方法按模式频率进行比例采样。Fips采用多步采样流程，解决了数值数据中的核心挑战：准确计算每个对象被多少区间模式覆盖。进一步提出HFips，使采样比例同时依赖于模式频率和超体积（hyper-volume）。这些方法有效应对了模式采样中普遍存在的长尾现象。我们形式化证明了Fips按频率采样，HFips按频率与超体积乘积采样。在多个数据集上的实验表明，所获模式质量高，且对长尾现象具有鲁棒性。
原文摘要 · Abstract (English)
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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