Tree-based Algorithm for Discovering Extended Action-Rules (System DEAR2)
Tree-based Algorithm for Discovering Extended Action-Rules (System DEAR2)
复制标题
用于发现扩展操作规则的基于树的算法（系统 DEAR2）
DOI：
10.1007/978-3-540-39985-8_53
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Alicja Wieczorkowska
中科院分区：
文献类型：
--
作者：
Li;Z. Ras;Alicja Wieczorkowska
在[3]中引入并在[5]中进一步研究的动作规则假设数据库中的属性分为两组：稳定和灵活。一般来说，一个动作规则可以由两个先前从同一个数据库中提取的规则构成。此外，我们假设这两个规则描述了两个不同的决策类，我们的目标是将一些对象从其中一个决策类重新分类到另一个决策类。灵活属性提供了一种工具，用于向用户提示给定的对象组需要灵活属性的某些值内的什么变化，以将这些对象重新分类到另一个决策类。在[4]中，为了构建动作规则，算法已经考虑了定义不同决策类的所有规则对。在本文中，我们提出了一个新的算法，这将显着减少的数量对规则需要检查的动作规则的建设和相同的加快整个过程。
Action rules introduced in [3] and investigated further in [5] assume that attributes in a database are divided into two groups: stable and flexible. In general, an action rule can be constructed from two rules extracted earlier from the same database. Furthermore, we assume that these two rules describe two different decision classes and that our goal is to re-classify some objects from one of these decision classes to the other one. Flexible attributes provide a tool for making hints to a user what changes within some values of flexible attributes are needed for a given group of objects to re-classify these objects to another decision class. In [4], to build action rules, all pairs of rules defining different decision classes have been considered by the algorithm. In this paper we propose a new algorithm which will significantly decrease the number of pairs of rules needed to be checked for action rules construction and the same speed up the whole process.