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      <title>Article: Understanding and Applying Correspondence Analysis</title>
      <link>https://www.infoq.com/articles/knime-correspondence-analysis/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Data+Analysis-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/knime-correspondence-analysis/en/headerimage/generatedHeaderImage-1677000367450.jpg"/&gt;&lt;p&gt;Customer segments, personality profiles, social classes, and age generations are examples of effective references to larger groups of people sharing similar characteristics. Correspondence analysis (CA) is a multivariate analysis technique that projects categorical data into a numeric feature space which captures most of the variability in the data by fewer dimensions.&lt;/p&gt; &lt;i&gt;By Maarit Widmann, Alfredo Roccato&lt;/i&gt;</description>
      <category>Data Analysis</category>
      <category>Statistics</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Thu, 23 Feb 2023 09:00:00 GMT</pubDate>
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      <dc:creator>Maarit Widmann, Alfredo Roccato</dc:creator>
      <dc:date>2023-02-23T09:00:00Z</dc:date>
      <dc:identifier>/articles/knime-correspondence-analysis/en</dc:identifier>
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