摘要
本文以面向大数据的高维数据挖掘技术为研究对象，运用邻接矩阵及其有向超图检测关联规则项之间的关系，探索了在大收据条件下关联规则冗余的检测法、基于生成树的分类去除算法，该算法能有效地提高关联规则的挖掘效率、降低实际处理所需要的时间。
Using the adjacency matrix and directed hypergraph detection relationship between association rule i- tems, this paper takes the high dimensional data for large data mining technology as the research object, explores the method and classification based on spanning tree removal algorithm for detection of large data condition redundant asso- ciation rules. The algorithm can effectively improve the efficiency of association rule mining and reduce the actual pro- cessing time.
出处
《职教与经济研究》
2015年第2期59-62,共4页
Vocational Education and Economy Research
基金
湖南省教育教学改革项目《基于职业岗位能力培养的高职综合实训课程开发与实践——以软件技术专业为例》(编号ZJB2013012)
娄职院科研项目《基于ANDROID平台的字符识别预处理算法研究与实现》(编号2013ZK007)
教研项目《以职业能力本位理念开发JAVA程序设计课程》(编号LZJY13ZZC10)阶段成果
关键词
大数据
超图
冗余关联规则
Large data
hypergraph
redundant association rules
