I am exploring the flexibility of partitional clustering algorithms. In particular, I would like to introduce more general distances than the ones which are used by default.
Let us consider, for simplicity,
dbscan contained in the R-package
fpc. It allows the user to specify a "data matrix, data.frame, dissimilarity matrix or dist-object".
My idea would be to compute the distance matrix of the given data w.r.t. my chosen distance, and run
Here comes the point where I am stuck. Is it true that specifying a distance matrix should lead inevitably to a hierarchical clustering? My intuition says that a hierarchical clustering in presence of a distance matrix makes more sense that a partitional one on the elements of the matrix itself. As I am no expert in clustering, I cannot judge the above statement properly.
Would you use a partitional algorithm on the distance matrix of a given dataset? Is this correct?
Thank you all!