So say suppose I have a data-set with features being either present or not i.e.
1. Now I want to identify the features which really help in the clustering.
Like say I have 4 training examples. Now say I have a feature which is present i.e =
1 in all the training example, thus I can remove the feature as it dies not help me. Now let's talk about 2 more features, if the number of training examples they are present in common is high, they also do not help much in clustering (think of 2 highly overlapping circles in a Venn Diagram). So in this way I want to find features which has a significant impact on the clustering i.e mostly non-overlapping features.
Is there any good way to do this? (my features are all represented in binary, either it is there or it is not).