An Efficient Map-Reduce Framework to Mine Periodic Frequent Patterns.
Lecture Notes in Computer Science(2017)
摘要
Periodic Frequent patterns (PFPs) are an important class of regularities that exist in a transactional database. In the literature, pattern growth-based approaches to mine PFPs have be proposed by considering a single machine. In this paper, we propose a Map-Reduce framework to mine PFPs by considering multiple machines. We have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines. Further, the notion of partition summary has been proposed to reduce the amount of data shuffled among the machines. Experiments on Apache Spark's distributed environment show that the proposed approach speeds up with the increase in number of machines and the notion of partition summary significantly reduces the amount of data shuffled among the machines.
更多查看译文
关键词
Data mining,Periodic frequent pattern mining,Map Reduce
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络