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    <title>topic Re: Proc Similarity to cluster time series data in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Similarity-to-cluster-time-series-data/m-p/135568#M7053</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thank you so much.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Thu, 12 Jun 2014 19:37:59 GMT</pubDate>
    <dc:creator>neilxu</dc:creator>
    <dc:date>2014-06-12T19:37:59Z</dc:date>
    <item>
      <title>Proc Similarity to cluster time series data</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Similarity-to-cluster-time-series-data/m-p/135566#M7051</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;If, for example, I have monthly sales number for different department (total 10 departments). Now I want to look at the trend of these sales based on departments. However 10 is too much so naturally clustering/grouping comes to my mind.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I saw the example of proc similarity to cluster time series and followed it to create the clusters. Now my question is I can use proc corr to group these departments, right? what is the benefit to do proc similarity?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 10 Jun 2014 13:19:26 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-Similarity-to-cluster-time-series-data/m-p/135566#M7051</guid>
      <dc:creator>neilxu</dc:creator>
      <dc:date>2014-06-10T13:19:26Z</dc:date>
    </item>
    <item>
      <title>Re: Proc Similarity to cluster time series data</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Similarity-to-cluster-time-series-data/m-p/135567#M7052</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hello, &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I assume you are following this example &lt;A href="http://support.sas.com/documentation/onlinedoc/ets/ex_code/131/smyex05.html" title="http://support.sas.com/documentation/onlinedoc/ets/ex_code/131/smyex05.html"&gt;SAS/ETS User's Guide Example Programs&lt;/A&gt;.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;As you can see, after SIMILARITY gives you the similarity matrix, then you can cluster in the same way you would use cross sectional clustering routines.&amp;nbsp; Use PROC CORR, CLUSTER, whatever you wish. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;A href="http://support.sas.com/documentation/cdl/en/etsug/66840/HTML/default/viewer.htm#etsug_similarity_overview.htm"&gt;Similarity &lt;/A&gt;has a number of utilities but all are related to temporal ordering.&amp;nbsp; Typical methods of clustering ignore the ordering.&amp;nbsp; In the time series version of this clustering we are looking for variables(series) that we can treat as a group, rather than observations that we treat as a group.&amp;nbsp; The SIMILARITY procedure effectively transposes this information (with some other tweaks) so the clustering can be done on the variables.&amp;nbsp; If you were to use clustering directly (a perfectly sensible practice for some uses) then you would effectively be looking for intervals that behave similarly.&amp;nbsp; This might be perfectly reasonable for some sort of time series segmentation but that is not what we are showing in this example.&amp;nbsp; &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Hope this helps-Ken &lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 12 Jun 2014 15:33:48 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-Similarity-to-cluster-time-series-data/m-p/135567#M7052</guid>
      <dc:creator>ets_kps</dc:creator>
      <dc:date>2014-06-12T15:33:48Z</dc:date>
    </item>
    <item>
      <title>Re: Proc Similarity to cluster time series data</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Similarity-to-cluster-time-series-data/m-p/135568#M7053</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thank you so much.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 12 Jun 2014 19:37:59 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-Similarity-to-cluster-time-series-data/m-p/135568#M7053</guid>
      <dc:creator>neilxu</dc:creator>
      <dc:date>2014-06-12T19:37:59Z</dc:date>
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