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<records>

  <record>
    <language>eng</language>
          <publisher>Oriental Scientific Publishing Company</publisher>
        <journalTitle>Biosciences Biotechnology Research Asia</journalTitle>
          <issn>0973-1245</issn>
            <publicationDate>2015-04-28</publicationDate>
    
        <volume>12</volume>
        <issue>1</issue>

 
    <startPage>609</startPage>
    <endPage>617</endPage>

	    <publisherRecordId>5940</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">Application of Principal Components Analysis Results in Visual Network Analysis</title>

    <authors>
	 


      <author>
       <name>Andrey Sergeevich Denisenko</name>

 
		
	<affiliationId>1</affiliationId>
      </author>
    

	 


      <author>
       <name>Grigory Olegovich Krylov</name>


		
	<affiliationId>2</affiliationId>

      </author>
    

	

	


	


	
    </authors>
    
	    <affiliationsList>
	    
		
		<affiliationName affiliationId="1">National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409, Russia, Moscow, Kashirskoe highway, 31. </affiliationName>
    

		
		<affiliationName affiliationId="2">Doctor of Physical and Mathematical Sciences, Financial University under the Government of the Russian Federation, 125993, Russia, Moscow, Leningradsky prospekt, 49.</affiliationName>
    
		
		
		
		
	  </affiliationsList>






    <abstract language="eng">The paper deals with the application of principal components analysis in a
roleof a preprocessor of the source data and its role in visual network analysis process.
Such kind of PCA application provides highlighting the most valuable objects in the
source selection. By the example of analyzing financial data of companies of certain
industry in order to measure their activity level authors show that principal components
analysis could be used as a preprocessor for further analysis. As a result of the research,
they show the integration and visualization of the integral scores in the process of visual
network analysis and their role in simplifying the large data processing.</abstract>

    <fullTextUrl format="html">https://www.biotech-asia.org/vol12no1/application-of-principal-components-analysis-results-in-visual-network-analysis/</fullTextUrl>



      <keywords language="eng">
        <keyword>Principal component analysis; Visual network analysis; Financial flows</keyword>
      </keywords>

  </record>
</records>