Frequent sequence mining methods often make use of constraints to control which subsequences should be mined. A variety of such subsequence constraints has been studied in the literature, including length, gap, span, regular-expression, and hierarchy constraints. In this article, we show that many subsequence constraints—including and beyond those considered in the literature—can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. In more detail, we propose a set of simple and intuitive “pattern expressions” to describe subsequence constraints and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our algorithms translate pattern expressions to succinct finite-state transducers, which we use as computational model, and simulate these transducers in a way suitable for frequent sequence mining. Our experimental study on real-world datasets indicates that our algorithms—although more general—are efficient and, when used for sequence mining with prior constraints studied in literature, competitive to (and in some cases superior to) state-of-the-art specialized methods.

中文翻译：

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具有子序列约束的频繁序列挖掘的统一框架

频繁的序列挖掘方法通常利用约束来控制应该挖掘哪些子序列。文献中已经研究了各种这样的子序列约束，包括长度、间隙、跨度、正则表达式和层次约束。在本文中，我们展示了许多子序列约束——包括和超出文献中考虑的那些——可以统一在一个框架中。统一处理允许研究人员联合研究多种类型的子序列约束（而不是单独研究每一种），并有助于提高从业者模式挖掘系统的可用性。更详细地说，我们提出了一组简单直观的“模式表达式”来描述子序列约束，并探索在这种一般约束下有效挖掘频繁子序列的算法。我们的算法将模式表达式转换为简洁的有限状态换能器，我们将其用作计算模型，并以适合频繁序列挖掘的方式模拟这些换能器。我们对现实世界数据集的实验研究表明，我们的算法（尽管更通用）是有效的，并且当用于具有文献中研究的先验约束的序列挖掘时，可以与（并且在某些情况下优于）最先进的技术相媲美专门的方法。