数据库中相关知识发现的一种多策略方法

数据库中相关知识发现的一种多策略方法(中文4200字,英文3100字)
Univ. Dortmund, Computer Science Department, LS VIII, D–44221 Dortmund
作者: Ryszard S. Michalski and Janusz Wnek
摘要：当学习非常大的数据库时，减少复杂性是非常重要的。两数据库中的知识发现（KDD）极端可行提出了。一个极端是选择一个非常简单的假设的语言，从而能够很快的学习在现实世界的数据库。另一个极端是选择一个小的数据集，从而能够学习非常富有表现力的（一阶逻辑）假设。多策略的方法允许一个包括大部分的这些优势，排除大量的缺点。简单的学习算法是用来检测层次结构的假设空间一个更复杂的学习算法。这种结构更好的假设空间，更好的学习可以修剪了无趣的或丢失的假设和更快的成为。
我们结合了归纳逻辑程序设计（ILP）直接与关系数据库管理系统。ILP算法控制在模型驱动的方式由用户在通过三个简单的学习算法引起的结构，数据驱动的方式。
关键词：数据库中的知识发现  归纳逻辑编程  函数依赖  数值间隔  背景知识
数据库中相关知识发现的一种多策略方法

A Multistrategy Approach to
Relational Knowledge Discovery in Databases
Univ. Dortmund, Computer Science Department, LS VIII, D–44221 Dortmund
Editor: Ryszard S. Michalski and Janusz Wnek

Abstract. When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on real-world databases.The opposite extreme is to select a small data set, thereby being able to learn very expressive (first-order logic) hypotheses. A multistrategy approach allows one to include most of these advantages and exclude most of the disadvantages. Simpler learning algorithms detect hierarchies which are used to structure the hypothesis space fora more complex learning algorithm. The better structured the hypothesis space is, the better learning can pruneaway uninteresting or losing hypotheses and the faster it becomes.
We have combined inductive logic programming (ILP) directly with a relational database management system.The ILP algorithm is controlled in a model-driven way by the user and in a data-driven way by structures that are induced by three simple learning algorithms.

Keywords: Knowledge discovery in databases, inductive logic programming, functional dependencies, numerical intervals, background knowledge.