Scalable Quick Reduct Algorithm - Iterative MapReduce Approach

被引：1
|
作者：
Singh, Praveen Kumar [1 ]
Prasad, P. S. V. S. Sai [2 ]
机构：
[1] Tata Consultancy Serv India, Hyderabad, Andhra Pradesh, India
[2] Univ Hyderabad, Sch Comp & Informat Sci, Hyderabad, Telangana, India
来源：
PROCEEDINGS OF THE THIRD ACM IKDD CONFERENCE ON DATA SCIENCES (CODS) | 2016年
关键词：
Rough sets; Reduct; Parallel/ Distributed Algorithms; Granular Computing; Twister; In_MRQRA; Iterative MapReduce; ATTRIBUTE REDUCTION;
D O I：
10.1145/2888451.2888476
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Feature selection by reduct computation is the key technique for knowledge acquistion using rough set theory. Existing MapReduce based reduct algorithms use Hadoop Map Reduce framework, which is not suitable for iterative algorithms. Paper aims to design and implementation of Iterative MapReduce based Quick reduct algorithm using Twister framework. The proposed In MRQRA Algorithm has partial granular level computations at mappers and granular computations at reducer. Experimental analysis on KDD-Cup99 dataset empirically established the relevence of proposed approach.
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页数：2
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