November 30, 2021 Journal article Open Access

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

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    <subfield code="a">Dimensionality Reduction, Data Mining, Independent Component Analysis, Knowledge Reduction, HDFS</subfield>
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    <subfield code="u">Ph.D, Department of Computer Science  and Engineering, Adikavi Nannaya University, Rajamahendravaram (A. P),  India.</subfield>
    <subfield code="a">R Krishnam Raju Indukuri</subfield>
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    <subfield code="u">Professor &amp; Dean of Academics  Department of Computer Science &amp; Engineering of Adikavi Nannaya  University, Rajamahendravaram (A. P), India</subfield>
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    <subfield code="u">Ph.D, Department of Computer Science  and Engineering, Adikavi Nannaya University, Rajamahendravaram (A. P),  India.</subfield>
    <subfield code="a">M. V. Rama Sundari</subfield>
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    <subfield code="p">International Journal of Recent Technology and Engineering (IJRTE)</subfield>
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    <subfield code="u">Ph.D, Department of Computer Science  and Engineering, Adikavi Nannaya University, Rajamahendravaram (A. P),  India.</subfield>
    <subfield code="a">K. B. V. Brahma Rao</subfield>
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    <subfield code="a">Evaluation of Various DR Techniques in  Massive Patient Datasets using HDFS</subfield>
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    <subfield code="a">&lt;p&gt;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.&lt;/p&gt;</subfield>
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