Using Accuracy to evaluate clustering performance Can I use accuracy, sensitivity, specificitity, positive predictive value, negative predictive value when i'm doing unsupervised classification (clustering) or do I have to limit my clustering evaluation to average silhouette width?
if you wonder how come I'm talking about accuracy while working on clustering, it's just because i have the actual real labels of all my subjects (healthy and sick) and I just want to see how well the clustering predicted the real labels with a specific variable. 
And please, if you have other relevant evaluation criteria do not hesitate!
 A: Let the classes have labels "healthy" and "sick".
If you run kmeans, you get clusters labeled 0,1,2,3,4
How would you compute any of these measures?
There are plenty of evaluation measures for clustering. They are related to classification measures, but not the same, for a reason... Use the clustering measures for cluster evaluation and the classification evaluation measures for classification evaluation.
The two most popular cluster evaluation measures seem to be ARI and NMI.
A: There is a major underlying hypothesis in your process. You are indeed assuming that the clustering returns two distinct groups, and each of them should corresponds to your labels, which may not be the case.
Keep in mind that clustering is a label-free process. The two groups and especially their labels may vary depending on the initialization. Your process needs to identify which one represents (supposedly) healthy subjects, and which one should represents sick subjects, regardless of the label returned by the clustering. 
Assuming that you have tackled this issue and identified who is who (say for instance, by looking at the group with highest proportion of truly sick subjects), you can indeed measure the accuracy of the model that you have created with the metrics you have mentioned. All metrics that can be applied to binary classification are relevant here. The biggest issue relies in the technique used to assign the right label to the groups created by your clustering.
