基于投影二维表的最大频繁模式挖掘算法  

Maximum Frequent Pattern Mining Algorithm Based on Projection Two-Dimensional Tables

作　　者：王利军[1] WANG Lijun(Department of Information Engineering,Anhui Institute of Economics Management,Hefei 230031,China)

机构地区：[1]安徽经济管理学院信息工程系

出　　处：《西昌学院学报（自然科学版）》2019年第4期70-74,共5页Journal of Xichang University(Natural Science Edition)

基　　金：安徽省高校自然科学重点项目：基于spark分布式计算平台的高校教学大数据分析方法研究（KJ2019A0965）

摘　　要：PITable-MAX是基于投影二维表的最大频繁模式挖掘算法。算法只需要遍历一次事务数据库,并将数据信息存入投影数据库中,在挖掘事务项时才会从投影数据库中提取相关的数据信息生成投影二维表,从而减少对内存空间的占用,算法虽然采用递归方式,但组合策略可实现减少条件投影二维表的规模,以达到减少空间;并结合减挖策略来减少挖掘事务项的个数,以达到提高挖掘效率的目的;实验验证了算法的可行性和优越性。PITable-MAX is a maximum frequent pattern mining algorithm based on projection two-dimensional tables. The algorithm only needs to traverse the transaction database once and store the data information in the projection database, and the related data information can be extracted from the projection database to generate a projection two-dimensional table when mining the transaction item, thus reducing the occupancy of memory space.Although the algorithm adopts recursive method, the combination strategy can reduce the size of conditional projection two-dimensional tables to reduce the space, and the number of mining items is reduced by combining the reduction strategy to improve the efficiency of mining. The experiment verifies the feasibility and superiority of the algorithm.

关 键 词：投影二维表 算法改进 最大频繁项集 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]