结合反向矩阵和频繁模式树方法的CP-nets结构学习  

CP-nets Structure Learning Combined with Inverted Matrix and Frequent Pattern Tree Method

作　　者：王卫星 刘兆伟 WANG Weixing;LIU Zhaowei(School of Computer and Control Engineering,Yantai University,Yantai 264005,China)

机构地区：[1]烟台大学计算机与控制工程学院,山东烟台264005

出　　处：《太原理工大学学报》2021年第2期282-291,共10页Journal of Taiyuan University of Technology

基　　金：国家自然科学基金资助项目(61572419);烟台市重点研发计划项目资助(2020XDRH092);烟台大学博士启动基金项目资助(JS19B77)。

摘　　要：提出一种基于反向矩阵结构在数据流上挖掘条件偏好和学习CP-nets的方法。利用反向矩阵的事务布局,减少了扫描数据库的次数,并且通过随机访问,在不到一次完整扫描的情况下得到频繁的偏好项。此外,通过建立频繁模式树FP-Tree,减少了候选项的生成。实验结果表明,与其他学习CP-nets结构的方法相比,该方法可以较快获得准确的CP-nets,在大型事务数据库方面表现出良好的性能,减少了内存需求。A method was preposed for mining conditional preferences and learning CP-nets on the data stream based on the inverted matrix structure.The transaction layout using the inverted matrix reduces the number of times the database is scanned,and through random access,frequent preference items can be found in less than one full scan.In addition,through the establishment of frequent preference tree FP-Tree,the generation of candidates is reduced.Experimental results show that,compared with other methods of learning CP-nets structure,this method can obtain accurate CP-nets faster,show better performance in large transaction databases,and reduce memory requirements.

关 键 词：CP-NETS 反向矩阵 数据流 频繁项集 FP-TREE 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]