文献类型：会议论文

英文题名：Research on Mining Global Maximal Frequent Itemsets for Health Big Data

作者：He, Bo Pei, Jianhui

第一作者：He, Bo

通信作者：He, B[1];He, B[2];He, B[3]

机构：[1]ChongQing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China;[2]ChengDu Univ Informat Technol, State Stat Bur, Key Lab Stat Informat Technol & Data Min, State Stat Bur, Chengdu 610103, Sichuan, Peoples R China;[3]Guizhou Inst Technol, Guizhou Key Lab Power Big Data, Guiyang 550003, Guizhou, Peoples R China

第一机构：ChongQing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China

通信机构：corresponding author), ChongQing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China;corresponding author), ChengDu Univ Informat Technol, State Stat Bur, Key Lab Stat Informat Technol & Data Min, State Stat Bur, Chengdu 610103, Sichuan, Peoples R China;corresponding author), Guizhou Inst Technol, Guizhou Key Lab Power Big Data, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院;

会议论文集：3rd IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

会议日期：OCT 03-05, 2017

会议地点：Chongqing, PEOPLES R CHINA

语种：英文

外文关键词：Data mining; FP-tree; Frequent Itemsets

年份：2017

摘要：Traditional mining algorithms did not suit mining of global maximal frequent itemsets. Therefore, a new mining algorithm of global maximal frequent itemsets for health big data, namely, NMAGMFI algorithm was proposed. Firstly, the global frequent items were mined. Secondly, local FP-tree was reconstructed by each node. Thirdly, the mining results were combined by the center node. Finally, the global maximal frequent itemsets are mining by the strategy of top-down and FP-tree. Experimental results suggest that NMAGMFI algorithm is fast.