Parallel attribute reduction algorithms using MapReduce

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• 作者：Jin Qian^a ; ^{qjqjlqyf@163.com" class="auth_mail} ; Duoqian Miao^b ; ^c ; ^{miaoduoqian@163.com" class="auth_mail} ; Zehua Zhang^b ; ^c ; ^{zehua.zzh@gmail.com" class="auth_mail} ; Xiaodong Yue^b ; ^d ; ^{yswantfly@gmail.com" class="auth_mail}
• 关键词：Rough set ; Parallel attribute reduction ; MapReduce ; Data mining
• 刊名：Information Sciences
• 出版年：20 September 2014
• 年：2014
• 卷：279
• 期：Complete
• 页码：671-690
• 全文大小：1104 K

文摘

Attribute reduction is the key technique for knowledge acquisition in rough set theory. However, it is still a challenging task to perform attribute reduction on massive data. During the process of attribute reduction on massive data, the key to improving the reduction efficiency is the effective computation of equivalence classes and attribute significance. Aiming at this problem, we propose several parallel attribute reduction algorithms in this paper. Specifically, we design a novel structure of i1" class="mathmlsrc">i1.gif&_user=111111111&_pii=S0020025514004666&_rdoc=1&_issn=00200255&md5=2e31f865c86891501e61f3e0940640c6" title="Click to view the MathML source">銆?em>key,value銆?/span>