<?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: Simulating clusters using K Means - Negative Values in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Simulating-clusters-using-K-Means-Negative-Values/m-p/847838#M102274</link>
    <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="https://community.jmp.com/t5/user/viewprofilepage/user-id/66125"&gt;@Alicia_500&lt;/a&gt;,&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Welcome in the Community !&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Clustering can be done with different algorithms, depending on your objectives, data types, and the criterion on which you are creating the clustering : based on distributions, on points density, on hierarchical structures/links between points, ...&lt;/P&gt;
&lt;P&gt;You can have a look at available algorithms based on your data types here :&amp;nbsp;&lt;A href="https://www.jmp.com/support/help/en/17.2/index.shtml#page/jmp/overview-of-platforms-for-clustering-observations.shtml#" target="_blank" rel="noopener noreferrer"&gt;Overview of Platforms for Clustering Observations&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If you need more infos about how to use the different algorithms, you can watch this video :&amp;nbsp;&lt;A href="https://www.jmp.com/en_ch/learning-library/topics/multivariate-methods/clustering.html" target="_blank" rel="noopener noreferrer"&gt;Clustering | JMP&lt;/A&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;There is also a very nice blog by&amp;nbsp;&lt;a href="https://community.jmp.com/t5/user/viewprofilepage/user-id/7567"&gt;@Chelsea-Parlett&lt;/a&gt;&amp;nbsp;explaining the differences between clustering methods :&amp;nbsp;&lt;A href="https://community.jmp.com/t5/JMP-Blog/Clustering-methods-for-unsupervised-machine-learning/ba-p/175053" target="_blank" rel="noopener"&gt;Clustering methods for unsupervised machine learning (jmp.com)&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Concerning your use case, with the relative low information provided and absence of data to test some approaches, I think K-Means may not be the best suitable clustering techniques as you're facing different distributions with different "spread". K-Means creates spherical clusters, as it doesn't assume any differences on the distributions.&lt;/P&gt;
&lt;P&gt;You could try using&amp;nbsp;&lt;A href="https://www.jmp.com/support/help/en/18.1/#page/jmp/normal-mixtures.shtml#" target="_blank" rel="noopener"&gt;Normal Mixtures&lt;/A&gt;, as it will be influenced by distributions and variances differences of your features or&amp;nbsp;&lt;A href="https://www.jmp.com/support/help/en/18.1/#page/jmp/hierarchical-cluster.shtml#" target="_blank" rel="noopener"&gt;Hierarchical Cluster&lt;/A&gt;, that doesn't assume any distributions for clustering. You could compare the outcomes of the clustering to see which one(s) make more sense, and the agreement between each method.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Hope I did understand your situation,&amp;nbsp;&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Fri, 14 Mar 2025 10:14:54 GMT</pubDate>
    <dc:creator>Victor_G</dc:creator>
    <dc:date>2025-03-14T10:14:54Z</dc:date>
    <item>
      <title>Simulating clusters using K Means - Negative Values</title>
      <link>https://community.jmp.com/t5/Discussions/Simulating-clusters-using-K-Means-Negative-Values/m-p/847828#M102273</link>
      <description>&lt;P&gt;Hi,&lt;/P&gt;&lt;P&gt;When I simulate clusters from the K Means platform I get some negative simulated values for one of my variables which, in practical terms, can only be positive.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Looking at the original distribution of this variable, it is non-normal and bounded at zero (so something like a log-normal distribution fits it well).&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Is there a way to ensure the data generated from the cluster simulation remains positive?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Many thanks,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Alicia&lt;/P&gt;</description>
      <pubDate>Fri, 14 Mar 2025 09:44:06 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Simulating-clusters-using-K-Means-Negative-Values/m-p/847828#M102273</guid>
      <dc:creator>Alicia_500</dc:creator>
      <dc:date>2025-03-14T09:44:06Z</dc:date>
    </item>
    <item>
      <title>Re: Simulating clusters using K Means - Negative Values</title>
      <link>https://community.jmp.com/t5/Discussions/Simulating-clusters-using-K-Means-Negative-Values/m-p/847838#M102274</link>
      <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="https://community.jmp.com/t5/user/viewprofilepage/user-id/66125"&gt;@Alicia_500&lt;/a&gt;,&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Welcome in the Community !&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Clustering can be done with different algorithms, depending on your objectives, data types, and the criterion on which you are creating the clustering : based on distributions, on points density, on hierarchical structures/links between points, ...&lt;/P&gt;
&lt;P&gt;You can have a look at available algorithms based on your data types here :&amp;nbsp;&lt;A href="https://www.jmp.com/support/help/en/17.2/index.shtml#page/jmp/overview-of-platforms-for-clustering-observations.shtml#" target="_blank" rel="noopener noreferrer"&gt;Overview of Platforms for Clustering Observations&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If you need more infos about how to use the different algorithms, you can watch this video :&amp;nbsp;&lt;A href="https://www.jmp.com/en_ch/learning-library/topics/multivariate-methods/clustering.html" target="_blank" rel="noopener noreferrer"&gt;Clustering | JMP&lt;/A&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;There is also a very nice blog by&amp;nbsp;&lt;a href="https://community.jmp.com/t5/user/viewprofilepage/user-id/7567"&gt;@Chelsea-Parlett&lt;/a&gt;&amp;nbsp;explaining the differences between clustering methods :&amp;nbsp;&lt;A href="https://community.jmp.com/t5/JMP-Blog/Clustering-methods-for-unsupervised-machine-learning/ba-p/175053" target="_blank" rel="noopener"&gt;Clustering methods for unsupervised machine learning (jmp.com)&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Concerning your use case, with the relative low information provided and absence of data to test some approaches, I think K-Means may not be the best suitable clustering techniques as you're facing different distributions with different "spread". K-Means creates spherical clusters, as it doesn't assume any differences on the distributions.&lt;/P&gt;
&lt;P&gt;You could try using&amp;nbsp;&lt;A href="https://www.jmp.com/support/help/en/18.1/#page/jmp/normal-mixtures.shtml#" target="_blank" rel="noopener"&gt;Normal Mixtures&lt;/A&gt;, as it will be influenced by distributions and variances differences of your features or&amp;nbsp;&lt;A href="https://www.jmp.com/support/help/en/18.1/#page/jmp/hierarchical-cluster.shtml#" target="_blank" rel="noopener"&gt;Hierarchical Cluster&lt;/A&gt;, that doesn't assume any distributions for clustering. You could compare the outcomes of the clustering to see which one(s) make more sense, and the agreement between each method.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Hope I did understand your situation,&amp;nbsp;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Fri, 14 Mar 2025 10:14:54 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Simulating-clusters-using-K-Means-Negative-Values/m-p/847838#M102274</guid>
      <dc:creator>Victor_G</dc:creator>
      <dc:date>2025-03-14T10:14:54Z</dc:date>
    </item>
    <item>
      <title>Re: Simulating clusters using K Means - Negative Values</title>
      <link>https://community.jmp.com/t5/Discussions/Simulating-clusters-using-K-Means-Negative-Values/m-p/848557#M102399</link>
      <description>&lt;P&gt;Thank you Victor for the reply - this is extremely helpful!&lt;/P&gt;</description>
      <pubDate>Wed, 19 Mar 2025 14:13:23 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Simulating-clusters-using-K-Means-Negative-Values/m-p/848557#M102399</guid>
      <dc:creator>Alicia_500</dc:creator>
      <dc:date>2025-03-19T14:13:23Z</dc:date>
    </item>
  </channel>
</rss>

