<p>Well This Depend on the Problem, the form of dataset and Class of Unsupervised algorithm used to solve the particular problem.</p>

<p>Roughly:-
Dimensionality reduction techniques are usually tested by calculating the error in reconstruction  so there we can use k-fold cross-validation procedure</p>

<p>But on clustering algorithm, I would suggest doing statistical testing in order to test performance. There is also little time-consuming trick which splitting dataset and hand label the test set  with meaningfull classes and cross validate</p>

<p>In any case unsupervised algorithm is used on supervised data then it always good cross-validate</p>

<p>overall:- It is not necessary to split data in the train-test set but if we can do it it is always better</p>

<p>Here is article which explains how cross-validation is a good tool for unsupervised learning
<a href="http://udini.proquest.com/view/cross-validation-for-unsupervised-pqid:1904931481/" rel="nofollow noreferrer">http://udini.proquest.com/view/cross-validation-for-unsupervised-pqid:1904931481/</a> and the full text is available here <a href="http://arxiv.org/pdf/0909.3052.pdf" rel="nofollow noreferrer">http://arxiv.org/pdf/0909.3052.pdf</a></p>

<p><a href="https:///www.researchgate.net/post/Which_are_the_methods_to_validate_an_unsupervised_machine_learning_algorithm" rel="nofollow noreferrer">https:///www.researchgate.net/post/Which_are_the_methods_to_validate_an_unsupervised_machine_learning_algorithm</a></p>
Is train/test-Split in unsupervised learning necessary/useful?
<p>In supervised learning I have the typical train/test split to learn the algorithm, e.g. Regression or Classification. Regarding unsupervised learning, my question is: Is train/test split necessary and useful? If yes, why?</p>