Sift - K-Means Dialog: Difference between revisions

From Software Product Documentation
Jump to navigation Jump to search
No edit summary
No edit summary
 
Line 16: Line 16:
<li><strong>Run Cumulative Variance Model</strong>: Can be used to determine the range of the K-Means test using variance explained instead of number of PCs</li>
<li><strong>Run Cumulative Variance Model</strong>: Can be used to determine the range of the K-Means test using variance explained instead of number of PCs</li>
<li><strong>Number of PCs (1-25)</strong>: The number of principal components representing the workspace.</li>
<li><strong>Number of PCs (1-25)</strong>: The number of principal components representing the workspace.</li>
<li><strong>Use Workspace Mean</strong>: Cluster based on the workspace mean instead of individual traces</li>
<li><strong>Scale PC Scores to Variance Explained</strong>: Normalizes the scale on the workspace scores using the variance explained</li>
<li><strong>Scale PC Scores to Variance Explained</strong>: Normalizes the scale on the workspace scores using the variance explained</li>
<li><strong>Use Custom Seed For First Centroid</strong>: Allows the selection of a custom seed instead of a randomly generated one, creates consistent results across runs.</li>


</ul>
</ul>

Latest revision as of 14:07, 30 April 2024

Language:  English  • français • italiano • português • español 

The K-Means button is found on the toolbar and under Outlier Detection.

  • Number of Clusters: The number of clusters to be calculated.
  • Maximum iterations: How many times the calculations will be run, more iterations will refine the results at the cost of longer processing times.
  • Run Cumulative Variance Model: Can be used to determine the range of the K-Means test using variance explained instead of number of PCs
  • Number of PCs (1-25): The number of principal components representing the workspace.
  • Scale PC Scores to Variance Explained: Normalizes the scale on the workspace scores using the variance explained
  • Use Custom Seed For First Centroid: Allows the selection of a custom seed instead of a randomly generated one, creates consistent results across runs.

Running a K-Means Test

A more in depth guide on the uses of K-Means and how to run one can be found here.

Retrieved from ""