Overfitting in an unsupervised technique
I'm not sure if this is valid but how about two trivial clustering examples:


Every object belongs to cluster which contain only this object. So for example if you would like to cluster N cars, there will be N clusters - one for each car.
On the other hand there could be case when algorithm will pick one cluster which will contain all elements inside it - one cluster with all N cars.


Those will be valid clusters but obviously they will not give you any useful information.