Suppose I have a set of data (with 2-dimensional feature space), and I want to obtain clusters from them. But I do not know how many clusters will be formed.
Yet, I want separate clusters (The number of clusters is more than 2).
I figured that k means of k medoid cannot be used in this case. Nor can I use hierarchical clustering. Also since there is no training set hence cannot use KNN classifier to any others (supervised learning cannot be used as no training set). I cannot use OPTICS algorithm as I do not want to specify the radius (I don't know the radius)
Is there any machine learning technique that would give me multiple clusters (distance based clustering) that deals well with outlier points too?
This should be the output: