A GPU-based Closed Frequent Itemsets Mining Algorithm over Stream

摘要：

Closed frequent itemsets are one of sevelral condensed representations of frequent itemsets, which store all the information of frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a problem that to the best of our knowledge has not been addressed, namely, how to use GPU to mine closed frequent itemsets in an incremental fashion. Our method employs a singleinstruction-multiple-data architecture to accelerate the mining speed using a bitmap data representation of frequent itemsets. Our experimental results show that our algorithm achieves a better performance in running time.

作者: Haifeng Li

作者单位: School of Information Central University of Finance and Economics Beijing China, 100081

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 6-10

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）