Sift - K-Means Dialog: Difference between revisions

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[[image:KM_dlg.png|right]]
[[image:sift_new_kmeans.png|right]]


The K-Means button is found on the toolbar and under [[image:sift_outlier_detection.png|30px]]<strong>Outlier Detection</strong>.
The K-Means button is found on the toolbar and under [[image:sift_outlier_detection.png|30px]]<strong>Outlier Detection</strong>.
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<ul>
<ul>


<li><strong>Number of Clusters</strong>:</li>
<li><strong>Number of Clusters</strong>: The number of clusters to be calculated.</li>
<li><strong>Maximum iterations</strong>:</li>
<li><strong>Maximum iterations</strong>: How many times the calculations will be run, more iterations will refine the results at the cost of longer processing times.</li>
<li><strong>Run Cumulative Variance Model</strong>:</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>:</li>
<li><strong>Number of PCs (1-25)</strong>: The number of principal components representing the workspace.</li>
<li><strong>Use Workspace Mean</strong>:</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>:</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>


==Running a K-Means Test==
==Running a K-Means Test==
A more in depth guide on the uses of K-Means and how to run one can be found [[file:Sift Tutorial: Run K-Means|here]].
A more in depth guide on the uses of K-Means and how to run one can be found [[Sift Tutorial: Run K-Means|here]].


[[Category:Sift]]
[[Category:Sift]]

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.

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