MapReduce-based H-mine algorithm

摘要：

　　Frequent Itemset Mining (FIM) is a very effective method for knowledge acquisition from data, but with the advent of the era of big data, traditional algorithms based on memory are facing severe challenges such as the computation speed and storage capacity. Fortunately, MapReduce model provides an efficient framework for distributed programming and operation framework. This paper proposes a novel MapReduce-based H-mine algorithm (MRH-mine), a version of H-mine algorithm in the distributed operation environment. Experimental results show that MRH-mine algorithm has a better performance and scalability than traditional H-Mine when facing massive data growth.

关键词： distributed data mining MapReduce H-mine parallelization

作者: Xingjie Feng Jie Zhao Zhiyuan Zhang

作者单位: computer science & technology CAUC Tian Jin,China

会议类型: 国际会议

会议名称: 2015 Fifth International Conference on Instrumentation and Measurement,Computer,Communication and Control (IMCCC2015)(第五届仪器测量、计算机通信与控制国际会议)

会议地点: 秦皇岛

会议语种:英文

页码: 1755-1760

在线出版日期: 2015-09-18（万方平台首次上网日期，不代表论文的发表时间）