An Improved Parallel Association Rules Algorithm Based on MapReduce Framework for Big Data

摘要：

　　Association rules mining is one of the most popular and significant issue in data mining and intends to discovery interest relations between variables in database.In our paper,we implemented an improved parallel Apriori algorithm which realized both count and candidate generation steps under MapReduce framework,while existing parallel Apriori algorithm only considered count step.We analyzed the time complexity of our improved parallel algorithm and compared to the original parallel algorithm,which indicates advantages of our algorithm with massive candidate item sets.Based on our experiment result,we proved that our algorithm performs better under big data situation and achieves excellent speedup feature.

关键词： Association Rules Apriori Hadoop MapReduce Data Mining

作者: Xinhao Zhou Yongfeng Huang

作者单位: Department of Electronic Engineering Tsinghua University Beijing,China

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 292-296

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）