基于广义归纳逻辑因果模型的知识发现与算法实现  

Algorithm and Realization of KDD Based on the Generalized Inductive Logical Causal Model

作　　者：张德政[1] 石阳[2] 邓一[1] 杨炳儒[1] 

机构地区：[1]北京科技大学信息工程学院,北京100083 [2]鞍山科技大学计算机系,鞍山114002

出　　处：《北京科技大学学报》2003年第4期377-381,共5页Journal of University of Science and Technology Beijing

基　　金：国家自然科学基金重点项目(No.69835001)

摘　　要：以语言场、广义细胞自动机和广义归纳逻辑因果模型为理论依据,分析了广义因果联系类知识的发现机理,给出了因果联系类知识发现的实现算法.该算法为解决具有随机不确定和模糊不确定性特征的因果联系类知识的发现提供了行之有效的方法.通过算法的运行实例,验证和说明了算法的正确性和有效性.Based on the fuzzy language field, generalized cell automation and generalized inductive logical causal model, the mechanism of causal relation knowledge discovery were studied, and an algorithm of KDD with causal rules was proposed. With this algorithm, the causal relation knowledge discovery with uncertainty of random and fuzzy could be done effectively. The correctness and effectiveness of the algorithm was illustrated by an example.

关 键 词：数据挖掘 知识发现 广义归纳逻辑因果模型 广义因果联系 语言场 广义细胞自动机 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP182[自动化与计算机技术—计算机科学与技术]