Constraint Relaxations for Discovering Unknown Sequential Patterns
Constraint Relaxations for Discovering Unknown Sequential Patterns
复制标题
用于发现未知序列模式的约束松弛
DOI：
10.1007/978-3-540-31841-5_2
复制
发表时间：
2004
期刊：
影响因子：
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通讯作者：
Arlindo L. Oliveira
中科院分区：
文献类型：
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作者：
C. Antunes;Arlindo L. Oliveira
序列模式挖掘的主要缺点是它缺乏对用户期望的关注，并且发现的模式数量很多。然而，普遍接受的解决方案-使用的约束-近似挖掘过程中的一个验证什么是频繁的模式之间的指定的，而不是发现未知的和意想不到的patterns.In本文中，我们提出了一种新的方法来挖掘序列模式，保持专注于用户的期望，而不损害发现未知的模式。我们的方法是基于约束松弛的使用，它包括使用它们来过滤接受的模式在挖掘过程中。我们提出了一个层次的放松，适用于上下文无关的语言表示的约束，现有的放松（法律的，validandnaïve，以前提出的）进行分类，并提出了几个新的类的放松。新的类别范围从近似的和不被接受的，到不同类型的弛豫的组成，如近似合法的或非前缀有效的弛豫。最后，我们提出了一个案例研究，显示了与应用这种方法来分析计算机科学专业学生的课程序列所取得的成果。
The main drawbacks of sequential pattern mining have been its lack of focus on user expectations and the high number of discovered patterns. However, the solution commonly accepted – the use of constraints – approximates the mining process to a verification of what are the frequent patterns among the specified ones, instead of the discovery of unknown and unexpected patterns.In this paper, we propose a new methodology to mine sequential patterns, keeping the focus on user expectations, without compromising the discovery of unknown patterns. Our methodology is based on the use of constraint relaxations, and it consists on using them to filter accepted patterns during the mining process. We propose a hierarchy of relaxations, applied to constraints expressed as context-free languages, classifying the existing relaxations (legal,validandnaïve, previously proposed), and proposing several new classes of relaxations. The new classes range from theapproxandnon-accepted, to the composition of different types of relaxations, like theapprox-legalor thenon-prefix-validrelaxations. Finally, we present a case study that shows the results achieved with the application of this methodology to the analysis of the curricular sequences of computer science students.