text: user2594: I am using a few clustering algorithms as well as my own tool for protein sequences. Also, I have benchmarked clustering results (i.e. ground truth) to compare with the results. Is there any tool/software/script to generate evaluation metrics for the clustering results.
  The clustering results/benchmarked are in the following format
  1  Name1
  1  Name2
  2  Name3
  2  Name4
  2  Name5
  .....
  
  UPDATE: I am evaluating the clustering performance using scikit
  
  user3848: The definitive answer is no, that is why they are not so popular. There are metrics that can be used:
  
  you can examine "minimum distance" as a criterion under the assumption that the lowest distance between taxa represents the largest homology (e.g. if it is sequence data), which would be translate to other biological scenarios.
  resampling, e.g. bootstrapping, jack-knifing and examine the tree structure of each node against the biological plausibility of the answer being correct. 
  
  For both points a biological a priori, i.e. biological plausibility of assessing the tree structure is required to confirm/reject the clustering method.
  Clustering algorithms are not like the probabilistic methods, where the highest likelihood is the singular criteria and of course they do give different answers. Its a brief explanation I know but hope it makes sense.
  
  user2594: I am evaluating the clustering performance using scikit
  
original_text: 2594: <p>I am using a few clustering algorithms as well as my own tool for protein sequences. Also, I have benchmarked clustering results (i.e. ground truth) to compare with the results. Is there any tool/software/script to generate evaluation metrics for the clustering results.</p>
  
  <p>The clustering results/benchmarked are in the following format</p>
  
  <pre><code>1  Name1
  1  Name2
  2  Name3
  2  Name4
  2  Name5
  .....
  </code></pre>
  
  <p>UPDATE: I am evaluating the clustering performance using <a href="https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation" rel="nofollow noreferrer">scikit</a></p>
   3848: <p>The definitive answer is no, that is why they are not so popular. There are metrics that can be used:</p>
  
  <ul>
  <li>you can examine "minimum distance" as a criterion under the assumption that the lowest distance between taxa represents the largest homology (e.g. if it is sequence data), which would be translate to other biological scenarios.</li>
  <li>resampling, e.g. bootstrapping, jack-knifing and examine the tree structure of each node against the biological plausibility of the answer being correct. </li>
  </ul>
  
  <p>For both points a biological a priori, i.e. biological plausibility of assessing the tree structure is required to confirm/reject the clustering method.</p>
  
  <p>Clustering algorithms are not like the probabilistic methods, where the highest likelihood is the singular criteria and of course they do give different answers. Its a brief explanation I know but hope it makes sense.</p>
   2594: <p>I am evaluating the clustering performance using <a href="https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation" rel="nofollow noreferrer">scikit</a></p>
  