Research on Mining Global Maximal Frequent Itemsets for Health Big Data

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.

关键词： Data mining FP-tree Frequent Itemsets

作者: Bo He Jianhui Pei

作者单位: School of Computer Science and Engineering,ChongQing University of Technology,400054 ChongQing,China School of Computer Science and Engineering,ChongQing University of Technology,400054 ChongQing,China

会议类型: 国际会议

会议名称: 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference(ITOEC2017)(2017 IEEE 第3届信息技术与机电一体化工程国际学术会议)

会议地点: 重庆

会议语种:英文

页码: 1143-1146

在线出版日期: 2017-10-03（万方平台首次上网日期，不代表论文的发表时间）