A Novel Parallel Algorithm for Frequent Itemset Mining of Incremental Dataset

被引:2
|
作者
Xu, Lijun [1 ]
Zhang, Yun [1 ]
机构
[1] Shanghai Maritime Univ, Informat Sch, Shanghai, Peoples R China
关键词
frequent itemset; parallel mining; incremental mining;
D O I
10.1109/ICISCE.2015.18
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Most Algorithms for frequent itemset mining typically make the assumption that data is centralized or static. They may waste computational and I/O resources when the data is dynamic, and they impose excessive communication overhead when the data is distributed. As a result, the data mining process is harmed by slow response time. In this paper we propose a novel algorithm that uses overlapping data partitions and parallelizes the workload among machines efficiently. Experiments confirm that
引用
收藏
页码:41 / 44
页数:4
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