Journal article Open Access

Evaluation of Various DR Techniques in Massive Patient Datasets using HDFS

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

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:contributor>Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)</dc:contributor>
  <dc:creator>K. B. V. Brahma Rao</dc:creator>
  <dc:creator>R Krishnam Raju Indukuri</dc:creator>
  <dc:creator>P. Suresh Varma</dc:creator>
  <dc:creator>M. V. Rama Sundari</dc:creator>
  <dc:description>The objective of comparing various dimensionality techniques is to reduce feature sets in order to group attributes effectively with less computational processing time and utilization of memory. The various reduction algorithms can decrease the dimensionality of dataset consisting of a huge number of interrelated variables, while retaining the dissimilarity present in the dataset as much as possible. In this paper we use, Standard Deviation, Variance, Principal Component Analysis, Linear Discriminant Analysis, Factor Analysis, Positive Region, Information Entropy and Independent Component Analysis reduction algorithms using Hadoop Distributed File System for massive patient datasets to achieve lossless data reduction and to acquire required knowledge. The experimental results demonstrate that the ICA technique can efficiently operate on massive datasets eliminates irrelevant data without loss of accuracy, reduces storage space for the data and also the computation time compared to other techniques.</dc:description>
  <dc:source>International Journal of Recent Technology and Engineering (IJRTE) 10(4) 1-6</dc:source>
  <dc:subject>Dimensionality Reduction, Data Mining, Independent Component Analysis, Knowledge Reduction, HDFS</dc:subject>
  <dc:subject>Retrieval Number</dc:subject>
  <dc:title>Evaluation of Various DR Techniques in  Massive Patient Datasets using HDFS</dc:title>
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