基于非线性相关发现的数据挖掘算法  

Data Mining Algorithm Based on Non-linear Correlation Discovery

作　　者：刘博[1] 郑启伦[2] 彭宏[2] 

机构地区：[1]华南师范大学教育信息技术学院,广东广州510631 [2]华南理工大学计算机科学与工程学院,广东广州510640

出　　处：《计算机应用研究》2007年第3期47-49,共3页Application Research of Computers

基　　金：广东省科技攻关项目(A10202001);广州市科技攻关项目(2004Z22D0091);广东省自然科学基金资助项目(031454)

摘　　要：现有的关联规则挖掘算法均致力于频繁集搜索,基于预先设置的支持度—置信度之上,具有很大的偶然性,不利于控制;并且关联规则没有体现数据整体的相关性。为了克服以上缺点,引入了非线性相关的概念,应用于不同相关类型规则的挖掘,且无须人为设置参数,从而大大提高了规则发现的实效性。The existing arithmetic of association rules mining techniques all do take up with searching the frequent setting, especially on the basis of support-believe degree set beforehand. So the searching has much more chance going against control. In addition, the association rules are out of correlatino of holistic data. Importing the concept of non-linear correlation, applying to different kinds of rules mining, which conquered those shortcoming discussed above. The proposed method need not set parameter man-made, and it enhances the substantial results of finding the rules greatly.

关 键 词：数据挖掘 关联规则挖掘 线性相关性发现 全局相关性 非线性相关发现 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]