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    <title>topic Re: Clustering with both Numerics and Categories data in SAS EM 13.1 in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251715#M3730</link>
    <description>Thank you for your solution to the problem. &lt;BR /&gt;</description>
    <pubDate>Tue, 23 Feb 2016 03:39:19 GMT</pubDate>
    <dc:creator>abuanuazu</dc:creator>
    <dc:date>2016-02-23T03:39:19Z</dc:date>
    <item>
      <title>Clustering with both Numerics and Categories data in SAS EM 13.1</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251496#M3726</link>
      <description>&lt;P&gt;Dear all,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am making Customer segmentation. My data included numeric data (4 variables) and categories data (7 variables).&lt;/P&gt;&lt;P&gt;I used clustering method to classified customer.&lt;/P&gt;&lt;P&gt;If I transfer categories data to dummy and then use K-Means algorithms for new data, It make falsifying clustering result or not?&lt;/P&gt;&lt;P&gt;Could you advice to me the way that I can use to cluster for both Numeric and Categories data on SAS?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks for your help!&lt;/P&gt;&lt;P&gt;Hue&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Mon, 22 Feb 2016 09:17:40 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251496#M3726</guid>
      <dc:creator>Hue</dc:creator>
      <dc:date>2016-02-22T09:17:40Z</dc:date>
    </item>
    <item>
      <title>Re: Clustering with both Numerics and Categories data in SAS EM 13.1</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251697#M3729</link>
      <description>&lt;P&gt;If you mixed up numeric and character variable , Check&amp;nbsp;Generalized Logits Model (a multinomial model&amp;nbsp;).&lt;/P&gt;</description>
      <pubDate>Tue, 23 Feb 2016 02:34:20 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251697#M3729</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2016-02-23T02:34:20Z</dc:date>
    </item>
    <item>
      <title>Re: Clustering with both Numerics and Categories data in SAS EM 13.1</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251715#M3730</link>
      <description>Thank you for your solution to the problem. &lt;BR /&gt;</description>
      <pubDate>Tue, 23 Feb 2016 03:39:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251715#M3730</guid>
      <dc:creator>abuanuazu</dc:creator>
      <dc:date>2016-02-23T03:39:19Z</dc:date>
    </item>
    <item>
      <title>Re: Clustering with both Numerics and Categories data in SAS EM 13.1</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251944#M3737</link>
      <description>&lt;P&gt;Clustering methods in EM convert nominal variables into dummy variables. There are different encoding techniques available in the cluster node.&amp;nbsp;&amp;nbsp;You can try those options and check the compactness of clusters. &amp;nbsp;If you are using procs, you can also try&amp;nbsp;&lt;SPAN&gt;PROC CLUSTER by using distance/similarity measures (such as jaccard).&lt;/SPAN&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 24 Feb 2016 02:17:24 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Clustering-with-both-Numerics-and-Categories-data-in-SAS-EM-13-1/m-p/251944#M3737</guid>
      <dc:creator>ilknurkabul</dc:creator>
      <dc:date>2016-02-24T02:17:24Z</dc:date>
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