摘要
提出了基于属性重要性的关联分类方法。与传统算法不同的是根据属性重要性程度生成类别关联规则;并且在构造分类器时改进了CBA算法中对于具有相同支持度、置信度规则选择时的随机性。实验结果证明,用该方法得到的分类规则与传统的关联分类算法相比,复杂度低,且有效提高了分类效果。
Associative classification rules based on importance of attributes is presented. Compared with other algorithms, this algorithm is proposed to apply the importance of attribute measure to the generation of candidate itemsets. Moreover, in the process of building classifier, a new strategy to rank class association rules in order to discriminate between rules which have identical confidences or supports and to prune rule redundancy and conflicts is proposed. The experiments show that, compared with CBA method, the method could filter out many candidate itemsets in the generation process, resulting in a much smaller set of rules, and can improve the efficiency of classification.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第9期2336-2338,2355,共4页
Computer Engineering and Design
基金
甘肃省教育厅科研基金项目(0603-10)
关键词
数据挖掘
关联分类
属性重要性
规则的优先度
数据库覆盖
data mining
associative classification rules
importance of attributes
global order of rules
database coverage