Parallel Computing For Mining Frequent Itemset

摘要：

Frequent Itemset l mining is a key step in association rule problem. Early classical algorithms ar.e serial algorithms, such as the Apriori 2, and FP-growth 3. With the rapid development of information technology, today, the amount of data often needed to handle is based on GB or TB, which has forced the efficiency of mining algorithms significantly improved. At present, more effective method is to use parallel computing to improve efficiency. In this regard, we propose a method based on partition of computing tasks to achieve parallel mining of frequent pattern, and has been experimentally verified in PC cluster.

关键词： data mining frequent itemset parallel computing

作者: Li Yi

作者单位: Department of Computer Science Chongqing University Chongqing, China

会议类型: 国际会议

会议名称: The 13th IEEE Joint International Computer Science and Information Technology Conference(2011年第13届IEEE联合国际计算机科学与信息技术会议 JICSIT 2011)

会议地点: 重庆

会议语种:英文

页码: 247-249

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）