other:inspect3d:tutorials:run_k-means
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| other:inspect3d:tutorials:run_k-means [2024/07/16 19:22] – created sgranger | other:inspect3d:tutorials:run_k-means [2025/01/16 20:47] (current) – wikisysop | ||
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| - | ====== | + | ====== |
| ==== Utility of K-Means ==== | ==== Utility of K-Means ==== | ||
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| It is assumed the groups are loaded, and a PCA has been completed. | It is assumed the groups are loaded, and a PCA has been completed. | ||
| - | {{means_tab.png}} | + | {{:k-means_tab.png}} |
| - | 1. Open the {{I3D_PCAOptions2.png}} **PCA Options** dropdown menu on the [[Other: | + | 1. Open the {{:I3D_PCAOptions2.png?20}} **PCA Options** dropdown menu on the [[Other: |
| - | 2. Click {{I3D_RunPCA.png}} **Run QA Using PCA**. | + | 2. Click {{:I3D_RunPCA.png?20}} **Run QA Using PCA**. |
| 3. A new dialog will display with option of a T-Squared and Q-Test, Custom Routine, Local Outlier Factor and K-Means. Select the K-Means tab. | 3. A new dialog will display with option of a T-Squared and Q-Test, Custom Routine, Local Outlier Factor and K-Means. Select the K-Means tab. | ||
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| 8. Select Run K-Means. | 8. Select Run K-Means. | ||
| - | 9. Close the {{I3D_RunPCA.png}} **Run QA Using PCA** dialog. | + | 9. Close the {{:I3D_RunPCA.png?20}} **Run QA Using PCA** dialog. |
| - | 10. Got to the {{I3DShowOptions.png}} **Show Options** dialog and change Plot Style to Cluster. The colors of the clusters are automatically set to the color palette. This can be changed by going to the Colour Palette Tab. | + | 10. Got to the {{:I3DShowOptions.png?20}} **Show Options** dialog and change Plot Style to Cluster. The colors of the clusters are automatically set to the color palette. This can be changed by going to the Colour Palette Tab. |
| - | 11. Close the {{I3DShowOptions.png}} **Show Options** dialog, and select the Workspace Scores in the PCA results. You should see the clusters of data points. | + | 11. Close the {{:I3DShowOptions.png?20}} **Show Options** dialog, and select the Workspace Scores in the PCA results. You should see the clusters of data points. |
| - | {{cluster.png}} | + | {{:options-cluster.png}} |
| ==== Interpreting K-Means Results ==== | ==== Interpreting K-Means Results ==== | ||
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| **Note:** Results may vary if different parameters were chosen. | **Note:** Results may vary if different parameters were chosen. | ||
| - | {{meansResults1.png}} | + | {{:k-meansResults1.png}} |
| Looking at the workspace tab we can select different points and the group and file will be displayed. This allows us to view which data points in a cluster belong to what group. We can clearly see the data points split into to clusters, light blue and dark blue, with somewhat of a separation. Light blue tends to be in the top right corner and dark blue tends to be in the bottom left. | Looking at the workspace tab we can select different points and the group and file will be displayed. This allows us to view which data points in a cluster belong to what group. We can clearly see the data points split into to clusters, light blue and dark blue, with somewhat of a separation. Light blue tends to be in the top right corner and dark blue tends to be in the bottom left. | ||
| - | {{MeansResults3.png}} | + | {{:k-MeansResults3.png}} |
| A K-means test finds the similarity between data points and groups them together into clusters. If you had two groups that were vastly different, the clusters would not have mixed groups. If the data points between groups have similarities the clusters may have data points from different groups. | A K-means test finds the similarity between data points and groups them together into clusters. If you had two groups that were vastly different, the clusters would not have mixed groups. If the data points between groups have similarities the clusters may have data points from different groups. | ||
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| If we look at the results from this K-Means we can see that the clusters are not a perfect representation of each group, signifying that there is some overlap and similarities between groups. The graph on the left shows the groups split up with the osteoarthritis group in purple and the normal group in blue. The graph on the right shows the two clusters. If we look at the light blue cluster it seems to be mainly the osteoarthritis group, and if we look at the dark blue cluster it seems to be mainly the normal group. The points circled in red show some osteoarthritis datapoints in the second cluster, again indicating some overlap. | If we look at the results from this K-Means we can see that the clusters are not a perfect representation of each group, signifying that there is some overlap and similarities between groups. The graph on the left shows the groups split up with the osteoarthritis group in purple and the normal group in blue. The graph on the right shows the two clusters. If we look at the light blue cluster it seems to be mainly the osteoarthritis group, and if we look at the dark blue cluster it seems to be mainly the normal group. The points circled in red show some osteoarthritis datapoints in the second cluster, again indicating some overlap. | ||
| - | {{MeansResults2.png}} | + | {{:k-MeansResults2.png}} |
other/inspect3d/tutorials/run_k-means.1721157752.txt.gz · Last modified: 2024/07/16 19:22 by sgranger