工程科学学报2016，Vol.38Issue(6)：886-892,7.DOI:10.13374/j.issn2095-9389.2016.06.020
基于定量关联规则树的分类及回归预测算法
Categorization and regression algorithm based on the quantitative association rule tree
摘要
Abstract
To solve the problem of the low efficiency and accuracy of numerical data mining based on the Apriori categorization association rule algorithm, this article introduces a categorization and regression algorithm based on the quantitative association rule tree. The modified quantitative association rule algorithm is adopted to mine numerical datasets to generate an association rule base, and the association rule tree ( QART) is reconstructed to realize the categorization and regression prediction. The results show that quantitative association based on the modified Apriori algorithm is helpful for improving the accuracy of categorization and regression and reducing the computational complexity, and the quantitative association rule tree can improve the efficiency of categorization and regression and increase the rule matching speed.关键词
数值挖掘/算法/关联规则/分类方法/回归方法Key words
data mining/algorithms/association rules/categorization methods/regression methods分类
信息技术与安全科学引用本文复制引用
王玲,李树林,吴璐璐..基于定量关联规则树的分类及回归预测算法[J].工程科学学报,2016,38(6):886-892,7.基金项目
国家自然科学基金资助项目(61572073) （61572073）
中央高校基本科研业务费资助项目(FRF-SD-12-009B) （FRF-SD-12-009B）
北京科技大学研究生教材专项基金资助项目 （）
