{"title":"可伸缩快速约简算法:迭代MapReduce方法","authors":"P. Singh, P. Prasad","doi":"10.1145/2888451.2888476","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Scalable Quick Reduct Algorithm: Iterative MapReduce Approach\",\"authors\":\"P. Singh, P. Prasad\",\"doi\":\"10.1145/2888451.2888476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":136431,\"journal\":{\"name\":\"Proceedings of the 3rd IKDD Conference on Data Science, 2016\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd IKDD Conference on Data Science, 2016\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2888451.2888476\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2888451.2888476","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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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