Train a classifier on cluster analysis results I am using HDBSCAN clustering in Python, to cluster my data. Next I want to use those thus labeled data to train a classifier (e.g., random forest) on these. The ultimate objective is to assign the label to a new unseen data. My doubt is, if it would be normal to obtain 100% train and/or test accuracy on the model? 
 A: Your whole approach seems problematic: why apply supervised learning on labels from clustering? You are adding additional noise!
In most settings, if you have labeled data, you can build a classification model using supervised learning techniques. If you do not have labeled data, you can run clustering to discover patterns of the data. It is not common to train a model based on labels obtained from clustering.
This is because 


*

*We may not sure the clustering results is good enough. There are many parameters in the algorithm (say number of clusters, or cutting threshold in hierarchical clustering), and verifying if the results is good is some separate task.

*If we have clustering results, we usually can "classify" future data into clusters based on the clustering results. For example, if you use k-means, you can use the centroid for future classification. I am not familiar with HDBSCAN though. 
Finally to your question about 100% on train or test accuracy. I would say it depends on your data and the model (supervised) you used. For example, if your data is "simple" (say linear separable), and your model is capturing your data. Then it is possible to have 100% training and testing accuracy. But for most real world data, it is less likely to get 100% on both.
In real world, anything can happen on testing data, and you never know what you will see in production. So, getting training accuracy too high may not be a good thing for over fitting reasons. 
A: There exist 'prediction' approaches possible with the HDBSCAN* framework by making use of the condensed tree data structure. This is similar to assigning new points to the closest centroid for K-Means. You can read more about it here.
A: Do not aim for 100% accuracy ever. That indicates you are overfitting.
With labels from clustering, overfitting is really bad, because the labels were not flawless in the first place!
But also: how would you use the answer? There is no automatic question you could answer; clusters are something to study, not to put into production.
