基于Map Reduce的序列模式挖掘算法

传统数据挖掘算法在处理海量数据集时计算能力有限。为解决该问题，提出一种基于Map Reduce的分布式序列模式挖掘算法MR-PrefixSpan。在PrefixSpan算法的基础上，对模式挖掘任务进行分割，利用Map函数处理由不同前缀得到的序列模式，并行构造投影数据库，从而提高挖掘效率及简化搜索空间。采用Reduce函数对中间结果进行规约，得到全局序列模式。在Hadoop集群上的实验结果表明，MR-PrefixSpan能减少数据库扫描时间，具有较高的并行加速比和较好的可扩展性。

Sequential Pattern Mining Algorithm Based on Map Reduce

Traditional data mining algorithm has computing power shortage in dealing with mass data set.Aiming at the problem,a distributed sequential pattern mining algorithm based on Map Reduce programming model named MR PrefixSpan is proposed.Mining tasks are decomposed to many,the Map function is used to mine each Prefix projected sequential pattern,and the projected databases are constructed parallelly.It simplifies the search space and acquires a higher mining efficiency.Then the intermediate values are passed to a Reduce function which merges together all these values to produce a possibly smaller set of values.Experimental results on Hadoop cluster show that MR PrefixSpan can reduce the time of scanning data base,has higher parallel speed up ratio and better expansibility.