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title: Choosing $k$ for selected features

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# Choosing $k$ for selected features

![](https://i.imgur.com/W8D4VCr.png)
![](https://i.imgur.com/gWjazh1.png)
![](https://i.imgur.com/QVI8qFd.png)
![](https://i.imgur.com/ZfyZ4GX.png)
![](https://i.imgur.com/MwGGcoT.png)

# Outcome for selected features $k=3$

<div class="lm-Widget p-Widget lm-Panel p-Panel jp-Cell-outputWrapper"><div class="lm-Widget p-Widget jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"><div class="jp-Collapser-child"></div></div><div class="lm-Widget p-Widget jp-OutputArea jp-Cell-outputArea" style=""><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>manual</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: SBP, PP, LVMI, PR, REEM, ESV_MODI, LAESVI, LA_GS, MVE_VEL, MVA_VEL, RMVEA, AM, EM, GS, MV_DECT
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI   </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1             </td><td>Cluster 2              </td></tr>
<tr><td>K-medoids       </td><td>0.102</td><td>2.936</td><td>0.168        </td><td>0: 721, 1: 24 (3.22%)</td><td>0: 293, 1: 28 (8.72%) </td><td>0: 244, 1: 97 (28.45%) </td></tr>
<tr><td>Gaussian Mixture</td><td>0.075</td><td>2.668</td><td>0.168        </td><td>0: 568, 1: 8 (1.39%) </td><td>0: 473, 1: 54 (10.25%)</td><td>0: 217, 1: 87 (28.62%) </td></tr>
<tr><td>K-Means         </td><td>0.058</td><td>2.337</td><td>0.165        </td><td>0: 466, 1: 5 (1.06%) </td><td>0: 572, 1: 48 (7.74%) </td><td>0: 220, 1: 96 (30.38%) </td></tr>
<tr><td>Agglomerative   </td><td>0.052</td><td>2.586</td><td>0.172        </td><td>0: 511, 1: 10 (1.92%)</td><td>0: 411, 1: 33 (7.43%) </td><td>0: 336, 1: 106 (23.98%)</td></tr>
<tr><td>Spectral        </td><td>0.027</td><td>2.533</td><td>0.169        </td><td>0: 335, 1: 1 (0.30%) </td><td>0: 652, 1: 49 (6.99%) </td><td>0: 271, 1: 99 (26.76%) </td></tr>
</tbody>
</table></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>normalized_cut_15</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: IVRT, AO_DIAM, SV_MODI, LVMI, IVSD, PR, PP, REAM, SBP, LVPWD, RWT, LA_GS, LVIDD, AM, RMVEA
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI   </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1             </td><td>Cluster 2              </td></tr>
<tr><td>K-medoids       </td><td>0.185</td><td>2.025</td><td>0.179        </td><td>0: 504, 1: 8 (1.56%) </td><td>0: 487, 1: 80 (14.11%)</td><td>0: 267, 1: 61 (18.60%) </td></tr>
<tr><td>Gaussian Mixture</td><td>0.152</td><td>3.351</td><td>0.176        </td><td>0: 617, 1: 10 (1.59%)</td><td>0: 307, 1: 63 (17.03%)</td><td>0: 334, 1: 76 (18.54%) </td></tr>
<tr><td>Agglomerative   </td><td>0.135</td><td>2.333</td><td>0.175        </td><td>0: 521, 1: 7 (1.33%) </td><td>0: 530, 1: 73 (12.11%)</td><td>0: 207, 1: 69 (25.00%) </td></tr>
<tr><td>Spectral        </td><td>0.117</td><td>2.299</td><td>0.178        </td><td>0: 356, 1: 3 (0.84%) </td><td>0: 488, 1: 43 (8.10%) </td><td>0: 414, 1: 103 (19.92%)</td></tr>
<tr><td>K-Means         </td><td>0.081</td><td>2.302</td><td>0.175        </td><td>0: 402, 1: 4 (0.99%) </td><td>0: 504, 1: 44 (8.03%) </td><td>0: 352, 1: 101 (22.30%)</td></tr>
</tbody>
</table></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>wkmeans_features</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: EM, RMVEA, REEM, LAEDVI, SBP, PP, IVSD, SM, MV_DECT, AM
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI    </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1             </td><td>Cluster 2              </td></tr>
<tr><td>Gaussian Mixture</td><td>0.223 </td><td>1.732</td><td>0.168        </td><td>0: 645, 1: 15 (2.27%)</td><td>0: 514, 1: 78 (13.18%)</td><td>0: 99, 1: 56 (36.13%)  </td></tr>
<tr><td>Agglomerative   </td><td>0.114 </td><td>1.916</td><td>0.170        </td><td>0: 554, 1: 10 (1.77%)</td><td>0: 608, 1: 87 (12.52%)</td><td>0: 96, 1: 52 (35.14%)  </td></tr>
<tr><td>K-Means         </td><td>0.064 </td><td>2.027</td><td>0.167        </td><td>0: 483, 1: 7 (1.43%) </td><td>0: 585, 1: 56 (8.74%) </td><td>0: 190, 1: 86 (31.16%) </td></tr>
<tr><td>K-medoids       </td><td>0.064 </td><td>1.923</td><td>0.166        </td><td>0: 437, 1: 5 (1.13%) </td><td>0: 600, 1: 49 (7.55%) </td><td>0: 221, 1: 95 (30.06%) </td></tr>
<tr><td>Spectral        </td><td>-0.009</td><td>2.215</td><td>0.171        </td><td>0: 337, 1: 1 (0.30%) </td><td>0: 626, 1: 47 (6.98%) </td><td>0: 295, 1: 101 (25.51%)</td></tr>
</tbody>
</table></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>lasso_features</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: REAM, LAEDVI, RMVEA, LA_ADI, EM, LA_ASI, MVA_VEL, IVSD, LVPWD, ESV_MODI
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI   </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0           </td><td>Cluster 1              </td><td>Cluster 2             </td></tr>
<tr><td>K-medoids       </td><td>0.249</td><td>1.693</td><td>0.177        </td><td>0: 561, 1: 8 (1.41%)</td><td>0: 574, 1: 103 (15.21%)</td><td>0: 123, 1: 38 (23.60%)</td></tr>
<tr><td>Gaussian Mixture</td><td>0.229</td><td>1.764</td><td>0.177        </td><td>0: 480, 1: 6 (1.23%)</td><td>0: 687, 1: 109 (13.69%)</td><td>0: 91, 1: 34 (27.20%) </td></tr>
<tr><td>Spectral        </td><td>0.208</td><td>1.688</td><td>0.175        </td><td>0: 386, 1: 3 (0.77%)</td><td>0: 577, 1: 58 (9.13%)  </td><td>0: 295, 1: 88 (22.98%)</td></tr>
<tr><td>Agglomerative   </td><td>0.186</td><td>1.779</td><td>0.177        </td><td>0: 405, 1: 4 (0.98%)</td><td>0: 707, 1: 94 (11.74%) </td><td>0: 146, 1: 51 (25.89%)</td></tr>
<tr><td>K-Means         </td><td>0.166</td><td>1.733</td><td>0.175        </td><td>0: 455, 1: 5 (1.09%)</td><td>0: 584, 1: 70 (10.70%) </td><td>0: 219, 1: 74 (25.26%)</td></tr>
</tbody>
</table></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>clustvarsel</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: LAEDVI, REAM, REEM, MVE_VEL, EM, AM, PP, RMVEA, MVA_VEL, MV_DECT
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI    </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1             </td><td>Cluster 2              </td></tr>
<tr><td>K-medoids       </td><td>0.216 </td><td>3.006</td><td>0.170        </td><td>0: 695, 1: 19 (2.66%)</td><td>0: 374, 1: 54 (12.62%)</td><td>0: 189, 1: 76 (28.68%) </td></tr>
<tr><td>Agglomerative   </td><td>0.086 </td><td>2.177</td><td>0.169        </td><td>0: 570, 1: 11 (1.89%)</td><td>0: 474, 1: 52 (9.89%) </td><td>0: 214, 1: 86 (28.67%) </td></tr>
<tr><td>K-Means         </td><td>0.019 </td><td>2.151</td><td>0.166        </td><td>0: 410, 1: 4 (0.97%) </td><td>0: 614, 1: 48 (7.25%) </td><td>0: 234, 1: 97 (29.31%) </td></tr>
<tr><td>Spectral        </td><td>-0.051</td><td>3.054</td><td>0.171        </td><td>0: 251               </td><td>0: 604, 1: 30 (4.73%) </td><td>0: 403, 1: 119 (22.80%)</td></tr>
<tr><td>Gaussian Mixture</td><td>-0.047</td><td>2.728</td><td>0.174        </td><td>0: 396, 1: 6 (1.49%) </td><td>0: 549, 1: 45 (7.58%) </td><td>0: 313, 1: 98 (23.84%) </td></tr>
</tbody>
</table></div></div></div></div>


# Outcome for selected features $k=2$

<div class="lm-Widget p-Widget jp-OutputArea jp-Cell-outputArea" style=""><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>manual</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: SBP, PP, LVMI, PR, REEM, ESV_MODI, LAESVI, LA_GS, MVE_VEL, MVA_VEL, RMVEA, AM, EM, GS, MV_DECT
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI   </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1              </td></tr>
<tr><td>Gaussian Mixture</td><td>0.255</td><td>1.968</td><td>0.176        </td><td>0: 743, 1: 24 (3.13%)</td><td>0: 515, 1: 125 (19.53%)</td></tr>
<tr><td>Spectral        </td><td>0.181</td><td>1.779</td><td>0.178        </td><td>0: 547, 1: 8 (1.44%) </td><td>0: 711, 1: 141 (16.55%)</td></tr>
<tr><td>K-Means         </td><td>0.173</td><td>1.727</td><td>0.174        </td><td>0: 672, 1: 11 (1.61%)</td><td>0: 586, 1: 138 (19.06%)</td></tr>
<tr><td>Agglomerative   </td><td>0.147</td><td>1.855</td><td>0.181        </td><td>0: 511, 1: 10 (1.92%)</td><td>0: 747, 1: 139 (15.69%)</td></tr>
<tr><td>K-medoids       </td><td>0.050</td><td>5.245</td><td>0.189        </td><td>0: 655, 1: 69 (9.53%)</td><td>0: 603, 1: 80 (11.71%) </td></tr>
</tbody>
</table></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>normalized_cut_15</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: IVRT, AO_DIAM, SV_MODI, LVMI, IVSD, PR, PP, REAM, SBP, LVPWD, RWT, LA_GS, LVIDD, AM, RMVEA
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI   </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1              </td></tr>
<tr><td>Gaussian Mixture</td><td>0.301</td><td>1.919</td><td>0.171        </td><td>0: 811, 1: 21 (2.52%)</td><td>0: 447, 1: 128 (22.26%)</td></tr>
<tr><td>Spectral        </td><td>0.243</td><td>1.682</td><td>0.179        </td><td>0: 527, 1: 7 (1.31%) </td><td>0: 731, 1: 142 (16.27%)</td></tr>
<tr><td>K-medoids       </td><td>0.233</td><td>1.762</td><td>0.180        </td><td>0: 538, 1: 10 (1.82%)</td><td>0: 720, 1: 139 (16.18%)</td></tr>
<tr><td>Agglomerative   </td><td>0.213</td><td>1.794</td><td>0.179        </td><td>0: 521, 1: 7 (1.33%) </td><td>0: 737, 1: 142 (16.15%)</td></tr>
<tr><td>K-Means         </td><td>0.179</td><td>1.698</td><td>0.176        </td><td>0: 643, 1: 11 (1.68%)</td><td>0: 615, 1: 138 (18.33%)</td></tr>
</tbody>
</table></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>wkmeans_features</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: EM, RMVEA, REEM, LAEDVI, SBP, PP, IVSD, SM, MV_DECT, AM
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI   </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1              </td></tr>
<tr><td>Gaussian Mixture</td><td>0.450</td><td>1.431</td><td>0.172        </td><td>0: 904, 1: 38 (4.03%)</td><td>0: 354, 1: 111 (23.87%)</td></tr>
<tr><td>K-medoids       </td><td>0.287</td><td>1.571</td><td>0.175        </td><td>0: 714, 1: 18 (2.46%)</td><td>0: 544, 1: 131 (19.41%)</td></tr>
<tr><td>K-Means         </td><td>0.193</td><td>1.522</td><td>0.175        </td><td>0: 663, 1: 13 (1.92%)</td><td>0: 595, 1: 136 (18.60%)</td></tr>
<tr><td>Spectral        </td><td>0.152</td><td>1.642</td><td>0.179        </td><td>0: 529, 1: 7 (1.31%) </td><td>0: 729, 1: 142 (16.30%)</td></tr>
<tr><td>Agglomerative   </td><td>0.151</td><td>1.737</td><td>0.179        </td><td>0: 554, 1: 10 (1.77%)</td><td>0: 704, 1: 139 (16.49%)</td></tr>
</tbody>
</table></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>lasso_features</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: REAM, LAEDVI, RMVEA, LA_ADI, EM, LA_ASI, MVA_VEL, IVSD, LVPWD, ESV_MODI
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
<tbody>
<tr><td>Name            </td><td>SI   </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1              </td></tr>
<tr><td>K-medoids       </td><td>0.303</td><td>1.753</td><td>0.176        </td><td>0: 790, 1: 29 (3.54%)</td><td>0: 468, 1: 120 (20.41%)</td></tr>
<tr><td>Gaussian Mixture</td><td>0.204</td><td>2.012</td><td>0.174        </td><td>0: 685, 1: 12 (1.72%)</td><td>0: 573, 1: 137 (19.30%)</td></tr>
<tr><td>Spectral        </td><td>0.172</td><td>1.795</td><td>0.179        </td><td>0: 502, 1: 6 (1.18%) </td><td>0: 756, 1: 143 (15.91%)</td></tr>
<tr><td>K-Means         </td><td>0.166</td><td>1.820</td><td>0.175        </td><td>0: 625, 1: 7 (1.11%) </td><td>0: 633, 1: 142 (18.32%)</td></tr>
<tr><td>Agglomerative   </td><td>0.128</td><td>1.771</td><td>0.182        </td><td>0: 405, 1: 4 (0.98%) </td><td>0: 853, 1: 145 (14.53%)</td></tr>
</tbody>
</table></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><h2>clustvarsel</h2></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedText jp-mod-trusted jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stdout"><pre>Features: LAEDVI, REAM, REEM, MVE_VEL, EM, AM, PP, RMVEA, MVA_VEL, MV_DECT
</pre></div></div><div class="lm-Widget p-Widget lm-Panel p-Panel jp-OutputArea-child"><div class="lm-Widget p-Widget jp-OutputPrompt jp-OutputArea-prompt"></div><div class="lm-Widget p-Widget jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output" data-mime-type="text/html"><table>
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<tr><td>Name            </td><td>SI   </td><td>DBI  </td><td>Gini impurity</td><td>Cluster 0            </td><td>Cluster 1              </td></tr>
<tr><td>K-medoids       </td><td>0.305</td><td>1.535</td><td>0.175        </td><td>0: 765, 1: 25 (3.16%)</td><td>0: 493, 1: 124 (20.10%)</td></tr>
<tr><td>Agglomerative   </td><td>0.149</td><td>1.664</td><td>0.179        </td><td>0: 570, 1: 11 (1.89%)</td><td>0: 688, 1: 138 (16.71%)</td></tr>
<tr><td>Gaussian Mixture</td><td>0.143</td><td>1.774</td><td>0.179        </td><td>0: 588, 1: 14 (2.33%)</td><td>0: 670, 1: 135 (16.77%)</td></tr>
<tr><td>K-Means         </td><td>0.126</td><td>1.613</td><td>0.178        </td><td>0: 581, 1: 11 (1.86%)</td><td>0: 677, 1: 138 (16.93%)</td></tr>
<tr><td>Spectral        </td><td>0.059</td><td>1.718</td><td>0.180        </td><td>0: 461, 1: 4 (0.86%) </td><td>0: 797, 1: 145 (15.39%)</td></tr>
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</table></div></div></div>