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    <title>topic Re: Cluster Analysis in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/Cluster-Analysis/m-p/308861#M4641</link>
    <description>&lt;P&gt;It might be a better idea to increase MAXCLUSTERS and to consider observations which end up alone in a cluster as outliers,&lt;/P&gt;</description>
    <pubDate>Wed, 02 Nov 2016 19:37:35 GMT</pubDate>
    <dc:creator>PGStats</dc:creator>
    <dc:date>2016-11-02T19:37:35Z</dc:date>
    <item>
      <title>Cluster Analysis</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Cluster-Analysis/m-p/308773#M4640</link>
      <description>&lt;P&gt;&lt;FONT color="#000080" face="Courier New" size="3"&gt;&lt;STRONG&gt;I have a data set for cluster analysis.&amp;nbsp;The code I used for the analysis is listed as below.&amp;nbsp;My question is that: Assuming there are 100 data points in the data set, how can I define the minimum number of data points is clustered in each group? &lt;/STRONG&gt;&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#000080" face="Courier New" size="3"&gt;&lt;STRONG&gt;Right now, I see some cluster, outputted by the current method, only contains 1 data point in a cluster.&amp;nbsp; This is why I am wondering if I can set a minimum number, such as the number of data points in a cluster must be more than 10% or 20%, etc.&amp;nbsp;for example.&amp;nbsp; &lt;/STRONG&gt;&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#000080" face="Courier New" size="3"&gt;&lt;STRONG&gt;Thank you for the help.&lt;/STRONG&gt;&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#000080" face="Courier New" size="3"&gt;&lt;STRONG&gt;PROC&lt;/STRONG&gt;&lt;/FONT&gt; &lt;STRONG&gt;&lt;FONT color="#000080" face="Courier New" size="3"&gt;FASTCLUS&lt;/FONT&gt;&lt;/STRONG&gt; &lt;FONT color="#0000ff" face="Courier New" size="3"&gt;DATA&lt;/FONT&gt;&lt;FONT face="Courier New" size="3"&gt;=model_data3&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;MAXC&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="3"&gt;&lt;FONT face="Courier New" size="3"&gt;=&lt;/FONT&gt;&lt;/FONT&gt;&lt;STRONG&gt;&lt;FONT color="#008080" face="Courier New" size="3"&gt;&lt;FONT color="#008080" face="Courier New" size="3"&gt;&lt;FONT color="#008080" face="Courier New" size="3"&gt;3&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;MAXITER&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="3"&gt;&lt;FONT face="Courier New" size="3"&gt;=&lt;/FONT&gt;&lt;/FONT&gt;&lt;STRONG&gt;&lt;FONT color="#008080" face="Courier New" size="3"&gt;&lt;FONT color="#008080" face="Courier New" size="3"&gt;&lt;FONT color="#008080" face="Courier New" size="3"&gt;100&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;REPLACE&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="3"&gt;&lt;FONT face="Courier New" size="3"&gt;=FULL&lt;/FONT&gt;&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;OUT&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="3"&gt;&lt;FONT face="Courier New" size="3"&gt;=WORK.CLKMKMeansData &lt;/FONT&gt;&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;&lt;FONT color="#0000ff" face="Courier New" size="3"&gt;VAR&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="3"&gt;&lt;FONT face="Courier New" size="3"&gt; volume;&lt;/FONT&gt;&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&lt;FONT color="#000080" face="Courier New" size="3"&gt;&lt;FONT color="#000080" face="Courier New" size="3"&gt;&lt;FONT color="#000080" face="Courier New" size="3"&gt;&lt;STRONG&gt;RUN&lt;/STRONG&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="3"&gt;&lt;FONT face="Courier New" size="3"&gt;;&lt;/FONT&gt;&lt;/FONT&gt;&lt;/P&gt;</description>
      <pubDate>Wed, 02 Nov 2016 15:39:45 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Cluster-Analysis/m-p/308773#M4640</guid>
      <dc:creator>wutao9999</dc:creator>
      <dc:date>2016-11-02T15:39:45Z</dc:date>
    </item>
    <item>
      <title>Re: Cluster Analysis</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Cluster-Analysis/m-p/308861#M4641</link>
      <description>&lt;P&gt;It might be a better idea to increase MAXCLUSTERS and to consider observations which end up alone in a cluster as outliers,&lt;/P&gt;</description>
      <pubDate>Wed, 02 Nov 2016 19:37:35 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Cluster-Analysis/m-p/308861#M4641</guid>
      <dc:creator>PGStats</dc:creator>
      <dc:date>2016-11-02T19:37:35Z</dc:date>
    </item>
    <item>
      <title>Re: Cluster Analysis</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Cluster-Analysis/m-p/308869#M4642</link>
      <description>&lt;P&gt;Since you have a single clustering variable, you could also try to fit a finite mixture model:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;PROC FMM DATA=model_data3;
model volume / kmax=3;
output out=CLKMKMeansData / group=volumeGroup;
run;&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;(untested)&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 02 Nov 2016 20:02:46 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Cluster-Analysis/m-p/308869#M4642</guid>
      <dc:creator>PGStats</dc:creator>
      <dc:date>2016-11-02T20:02:46Z</dc:date>
    </item>
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