A GPU-based Maximal Frequent Itemsets Mining Algorithm over Stream

摘要：

Maximal frequent itemsets are one of sevelral con densed representations of frequent itemsets, which store most of the information contained in 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 maximal frequent itemsets in an incremental fashion. Our method employs a single-instruction-multiple-data architecture to accelerate the mining speed with using a bitmap data representation of frequent itemsets; moreover, we use an inverse tree structure to prune efficiently. 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 International Conference on Computer and Communication Technologies in Agriculture Engineering(计算机与通信技术在农业工程国际会议 CCTAE 2010)

会议地点: 成都

会议语种:英文

页码: 289-292

在线出版日期: 2010-06-12（万方平台首次上网日期，不代表论文的发表时间）