Parallel mining of frequent patterns in transactional databases

被引:0
作者
Fakhrahmad, S. M. [1 ]
Fard, G. H. Dastghaibi [2 ]
机构
[1] Islamic Azad Univ Shiraz, Dept Comp Engn, Sch Engn, Shiraz, Iran
[2] Shiraz Univ, Sch Engn, Dept Comp Sci & Engn, Shiraz, Iran
来源
WORLD CONGRESS ON ENGINEERING 2008, VOLS I-II | 2008年
关键词
parallel processing; data mining; frequent itemsets; association rules; load balancing;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
One of the important and well-researched problems in data mining is mining association rules from transactional databases, where each transaction consists of a set of items. The main operation in this discovery process is computing the occurrence frequency of the interesting set of items. In practice, we are usually faced with large datasets, and an exponentially large space of candidate itemsets. A potential solution to the computation complexity is to parallelize the mining algorithm. In this paper, firstly, we introduce an already proposed sequential mining algorithm for discovery of frequent itemsets, which requires just a single scan of the database. In the next part, we present four parallel versions of the algorithm. The parallel algorithms will be compared analytically and experimentally, regarding some important factors, such as time complexity, communication rate, load balancing, etc.
引用
收藏
页码:605 / +
页数:2
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