• K.B.V. Brahma Rao
• P. Suresh Varma
• R Krishnam Raju Indukuri
• M V Rama Sundari

In order to eliminate redundancy of massive datasets, we developed parallel large-scale technique for knowledge reduction using rough set and MapReduce methods on patient massive datasets. Our technique will reduce the utilization of memory and processing time. The superfluous data is removed without significant accuracy loss using type of disease. In this paper we presented theoretical and experimental approach for knowledge reduction from large patient datasets using significance of attributes by organizing the data in discernibility and indiscernibility matrices. The experimental results demonstrate that the proposed parallel knowledge reduction method can efficiently process massive datasets on Hadoop platform, with highly speed up the grouping process and largely reduce the storage requirements. In all the experiments the introduced method based on significance of attributes is compared with the method based on positive region or information entropy. The comparison clearly shows that the former method outperforms the latter one.

Big Data, MapReduce, Rough Set, Knowledge Reduction, HDFS.

"Knowledge Reduction in Massive Patient Datasets Using Rough Set Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.416-423, November-2018, Available :http://www.jetir.org/papers/JETIR1811A52.pdf

"Knowledge Reduction in Massive Patient Datasets Using Rough Set Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp416-423, November-2018, Available at : http://www.jetir.org/papers/JETIR1811A52.pdf