基于模糊分类关联规则的支持向量机分类器生成方法  

Method of SVM classifier generation based on fuzzy classification association rule

作　　者：崔建[1] 李强[1] 刘勇 

机构地区：[1]空军雷达学院预警监视情报系,武汉430019 [2]京津地区军事代表室,北京100015

出　　处：《计算机应用》2011年第5期1348-1350,1366,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60736009)

摘　　要：为提高数据库分类系统的分类精度,提出一种新的分类方法。首先,利用模糊C-均值聚类算法对数据库中的连续属性进行离散化;然后,在此基础上提出一种改进的模糊关联算法挖掘分类关联规则;最后,通过计算规则和模式之间的兼容性指标来构造特征向量,构建支持向量机的分类器模型。实验结果表明,该方法具有较高的分类识别能力和分类效率。To increase the classification accuracy of the database classification system,this paper proposed a new classification method.Firstly,the continuous attributes were dispersed by the Fuzzy C-Mean（FCM） algorithm.Secondly,an improved fuzzy association method was proposed to mine the classification association rules.Eventually,the compatibility between the generated rules and patterns was used to construct a set of feature vectors,which were used to generate a classifier.The experimental results demonstrate that the method has high discrimination and efficiency.

关 键 词：数据挖掘 支持向量机 模糊关联规则 分类系统 离散化 模糊C-均值 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]