%0 Journal Article
%T Distributed association rules mining algorithm by sampling and meta-learning<br>利用抽样技术和元学习的分布式关联规则挖掘算法
%A LI Mei-hua
%A WANG Li-ming
%A XU Hong-tao
%A <br>李梅花
%A 王黎明
%A 许红涛
%J 计算机应用
%D 2006
%I 
%X A new distributed association rule mining algorithm of DASM was presented. It adopted the ideas of dynamic itemset counting and sampling, and produced frequent itemsets by meta-learning method. Different sites that applied DASM needn't share the same memory. To assure the completeness of the results, the concept of similar degree was introduced. Theory analysis and experiments on the datasets generated using the generator from the IBM Almaden Quest research group show that DASM has better performance and less communication loads. DASM is applicable to those applications where the efficiency could be more important than accuracy results.performance and less communication loads. DASM is applicable to those applications where the efficiency could be more important than accuracy results.
%K sampling
%K meta-learning
%K dynamic itemset counting
%K similar degree
%K distributed association rule mining<br>抽样
%K 元学习
%K 动态项集计数
%K 相似度
%K 分布式关联规则挖掘
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=5FE2C39302802AD3&yid=37904DC365DD7266&vid=96C778EE049EE47D&iid=E158A972A605785F&sid=11632AEF1E1F2092&eid=3893EBCAC6700388&journal_id=1001-9081&journal_name=计算机应用&referenced_num=1&reference_num=12