<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Re: SVD and Prob columns Output by Text Cluster Node in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/SVD-and-Prob-columns-Output-by-Text-Cluster-Node/m-p/257673#M9538</link>
    <description>&lt;P&gt;SAS Text Miner computes a term-by-document matrix A, where the i-th row and j-th column represents the number of times that the i-th term appears in the j-th document.&amp;nbsp; A has N rows where N is the number of terms in the corpus, and M columns where M is number of documents.&amp;nbsp; You can think of the M columns of A, each of which represents a document, as vectors (or points) in an N-dimensional term-frequency space. The SVD rotates the points so the most variation of your corpus of documents lie in the direction of the first coordinate SVD_1, the second coordinate SVD_2 points in an orthogonal direction that gives the next most variation in the corpus.&amp;nbsp; These vectors represent term-frequency profiles that are convenient. In fact the SVD helps reduce the dimensionality of the problems by only considering a limited number of directions. Since you have 16 SVD vectors, SAS has reduced your corpus to a 16 dimensional subspace of the full term-frequency space.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Clustering is more easily accomplished relative to the SVD coordinate system.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;The probability columns represent the likelihood that each document belongs to a cluster. If you have 10 clusters, there should only be 10 probability columns. Each document is assigned to the cluster that corresponds to the maximum likelihood. I am not sure about the details of how the probability is determined. I assume that it is something like a linear discriminant analysis.&lt;/P&gt;</description>
    <pubDate>Fri, 18 Mar 2016 18:00:03 GMT</pubDate>
    <dc:creator>BrianLoe</dc:creator>
    <dc:date>2016-03-18T18:00:03Z</dc:date>
    <item>
      <title>SVD and Prob columns Output by Text Cluster Node</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/SVD-and-Prob-columns-Output-by-Text-Cluster-Node/m-p/250525#M9537</link>
      <description>&lt;P&gt;For my dataset, Text Cluster Node produces 10 clusters, 16 SVD and 16 Prob columns/variables in the output dataset.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Questions:&lt;/P&gt;
&lt;P&gt;1) How are these 10 clusters related 16 SVD variables?&lt;/P&gt;
&lt;P&gt;2) Do 16 SVD variables represent "concepts" which are different from clusters?&lt;/P&gt;
&lt;P&gt;3) How are Prob variables computed?&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 17 Feb 2016 07:19:03 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/SVD-and-Prob-columns-Output-by-Text-Cluster-Node/m-p/250525#M9537</guid>
      <dc:creator>aha123</dc:creator>
      <dc:date>2016-02-17T07:19:03Z</dc:date>
    </item>
    <item>
      <title>Re: SVD and Prob columns Output by Text Cluster Node</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/SVD-and-Prob-columns-Output-by-Text-Cluster-Node/m-p/257673#M9538</link>
      <description>&lt;P&gt;SAS Text Miner computes a term-by-document matrix A, where the i-th row and j-th column represents the number of times that the i-th term appears in the j-th document.&amp;nbsp; A has N rows where N is the number of terms in the corpus, and M columns where M is number of documents.&amp;nbsp; You can think of the M columns of A, each of which represents a document, as vectors (or points) in an N-dimensional term-frequency space. The SVD rotates the points so the most variation of your corpus of documents lie in the direction of the first coordinate SVD_1, the second coordinate SVD_2 points in an orthogonal direction that gives the next most variation in the corpus.&amp;nbsp; These vectors represent term-frequency profiles that are convenient. In fact the SVD helps reduce the dimensionality of the problems by only considering a limited number of directions. Since you have 16 SVD vectors, SAS has reduced your corpus to a 16 dimensional subspace of the full term-frequency space.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Clustering is more easily accomplished relative to the SVD coordinate system.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;The probability columns represent the likelihood that each document belongs to a cluster. If you have 10 clusters, there should only be 10 probability columns. Each document is assigned to the cluster that corresponds to the maximum likelihood. I am not sure about the details of how the probability is determined. I assume that it is something like a linear discriminant analysis.&lt;/P&gt;</description>
      <pubDate>Fri, 18 Mar 2016 18:00:03 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/SVD-and-Prob-columns-Output-by-Text-Cluster-Node/m-p/257673#M9538</guid>
      <dc:creator>BrianLoe</dc:creator>
      <dc:date>2016-03-18T18:00:03Z</dc:date>
    </item>
  </channel>
</rss>

