Tuning of Parallel Frequent Pattern Growth Algorithm Based on Distributed Coordination System
基于分布式协调系统的并行频繁模式增长算法的优化

Frequent pattern mining can find frequent pattern in data, and iYs an important step in the association rules mining. Parallel frequent pattern(PFP) algorithms apply it into parallel environment, which is suitable for massive data.Based on the implementation of Apache Mahout, this paper proposed a design for optimizing the counting and sorting parts of PFP using distributed coordination system. This design takes advantage of distributed coordination system and reduces the consumption on HDFS and memory of data node. Another benefit is that the counting procedure and sorting procedure start parallclly. At last this paper analyzed the experimental result and the difficulties for implementation for further study.