基于关系数据库的关联规则挖掘算法DB-growth

Association rule mining algorithm DB-growth Algorithm bases on relational database

从大数据中挖掘隐藏的、多维的有价值的关联规则具有广泛的应用价值。关联规则挖掘经典算法Apriori存在重复扫描数据库并产生大量候选项集的瓶颈问题,FP-growth算法虽不产生候选集,但FP-tree不支持大数据的存储与遍历,不能有效支持大数据挖掘;另外,Apriori以及FP-growth算法实施增量挖掘都需要重构关联规则,不适用于增长型事务数据挖掘。针对这些问题,设计基于关系数据库表SourceIndex的DB-growth算法,采用模式组合生成模式串的方式,更新数据库构建频繁集,有效地提高了关联规则的挖掘效率,同时对增量挖掘及深度挖掘也能得到较好的支持。

Excavating potential,multidimensional valuable association rules from big data has wide application.The main association rule mining algorithm Apriori has the bottlenecks of scaning repeately database and generating big number of candidate sets,Though the FP algorithm does not generate candidate sets,but FP-tree can't handle the problem of storage and traversal of big data;In addition,Apriori and FP-growth algorithm needs to reconstruct association rules while implementing increment mining,its not available for growth-oritened data mining.Facing those problems,designing DB-growth algorithm based on relational database table SourceIndex,applying string combinate to generate pattern,insert or update database to construct frequent sets,mining association rules by querying database,in addition,it supports increment mining and depth mining.