基于平行网格变换的频繁项集生成算法  

An Algorithm for Frequent Item Set Generation Using Parallel Mesh Transposition

作　　者：马良斋[1] 周渊[2] 姜滨[1] Ma Liang-zhai Zhou Yuan Jiang Bing(School of Electronic and Information Engineering, Lanzhou Jiaotong University School of Chemical and Biological Engineering, Lanzhou Jiaotong University,Lanzhou Gansu 730070)

机构地区：[1]兰州交通大学电子与信息工程学院 [2]兰州交通大学化学与生物工程学院,甘肃兰州730070

出　　处：《河西学院学报》2016年第5期46-51,共6页Journal of Hexi University

基　　金：甘肃省自然科学基金项目(项目编号:1212RJZA059)

摘　　要：关联规则研究数据库中一组互不相属对象之间的相关性,挖掘出具有一定意义的关联关系、挖掘算法如Apriori、FP-Growth等,这些算法需要反复多次扫描整个数据库导致I/O负载增加,降低了CPU的性能.文章通过对数据库进行转置和平行变换以减少扫描的次数,从而提高算法效率.Association rules are interesting correlations among attributes in a database.It plays an important role in generating frequent item set from large database.It is discovered that interesting association relationship is among business transaction records in many business decision-making process,such as catalog decision,cross-marketing,and loss-leader analysis.It is also used to extract hidden knowledge from large datasets.The Association Rules Mining algorithms such as Apriori,FP-Growth require repeated scans over the entire database.All the input/output overheads are being generated during repeated scanning of the entire database and decreases the performance of CPU,memory and I/O overheads.The Algorithm uses databases in transposed form and database transposition is done using Parallel transposition algorithm so as to generate all significant association rules number of passes required reduced.

关 键 词：数据挖掘 关联规则挖掘(ARM) 关联规则 APRIORI算法 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]