<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>

CC BY-SA 4.0
2020-03-11T19:13:47.810Z
Thanks for the explanation. I am now trying  V-measure (https://www.aclweb.org/anthology/D07-1043.pdf) using sklearn (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.v_measure_score.html)
SBDK8219
CC BY-SA 4.0
2020-03-11T18:22:32.003Z
2020-03-11T18:22:32.003Z
M__
<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>

CC BY-SA 4.0
2020-03-12T18:09:12.773Z
Okay, but thats a different question. You appear to be implying you have input output associations for machine learning, this information was not present in the question.
M__
CC BY-SA 4.0
2020-03-12T20:38:14.163Z
I have mentioned that I have both output and ground truth. I am sorry for the confusion.
SBDK8219
CC BY-SA 4.0
2020-03-13T06:32:01.283Z
No problem, its just ML is a different approach for verification involving e.g. ROC and accurary indices. If you can do ML, then its really good.
M__
CC BY-SA 4.0
2020-03-16T19:57:32.287Z
Thanks, @Michael !! I think I have to use ROC for further analysis. There are some drawbacks using scikit for my problem
SBDK8219
CC BY-SA 4.0
2020-03-12T17:58:41.570Z
2020-03-12T17:58:41.570Z
SBDK8219
<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>

CC BY-SA 4.0
2020-03-12T10:43:55.933Z
from your other comment you have already found scikit, if your number of clusters/ground truth labels is reasonably low (say <50), take a look at the contingency matrix as well, it can give a very clear view of what is going on in your clustering https://scikit-learn.org/stable/modules/clustering.html#contingency-matrix
Pallie
CC BY-SA 4.0
2020-03-12T17:54:37.320Z
Thanks, @Pallie !! Yes, scikit looks good as some clustering papers used the same measures as well. The number of labels for my work is reasonably high (thousands and millions).
SBDK8219
<sequence-analysis><clustering>
Tools or softwares for clustering evaluation
# Tools or softwares for clustering evaluation

(Asked by: username_0 on 2020-03-11)

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

**Tags:** sequence-analysis, clustering
> **Comments:**
> **username_1**: from your other comment you have already found scikit, if your number of clusters/ground truth labels is reasonably low (say <50), take a look at the contingency matrix as well, it can give a very clear view of what is going on in your clustering https://scikit-learn.org/stable/modules/clustering.html#contingency-matrix  
> **username_0**: Thanks, @Pallie !! Yes, scikit looks good as some clustering papers used the same measures as well. The number of labels for my work is reasonably high (thousands and millions).  

---

## 2 answers

### Answer by username_2 on 2020-03-11. Score: 1 | Accepted answer

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.

> **Comments:**
> **username_0**: Thanks for the explanation. I am now trying  V-measure (https://www.aclweb.org/anthology/D07-1043.pdf) using sklearn (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.v_measure_score.html)  

---

### Answer by username_0 on 2020-03-12. Score: 2

I am evaluating the clustering performance using [scikit](https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation)

> **Comments:**
> **username_2**: Okay, but thats a different question. You appear to be implying you have input output associations for machine learning, this information was not present in the question.  
> **username_0**: I have mentioned that I have both output and ground truth. I am sorry for the confusion.  
> **username_2**: No problem, its just ML is a different approach for verification involving e.g. ROC and accurary indices. If you can do ML, then its really good.  
> **username_0**: Thanks, @Michael !! I think I have to use ROC for further analysis. There are some drawbacks using scikit for my problem  

---

