Late last night I started having a look into the K-Means algorithm. Seems easy as such. But.... as I want to keep a general approach so one could cluster any object it becomes a bit less a trivial task.
The problem/assumption of the K-Means algorithm is that the data-elements need to be representable in vector-space. This is something I cannot get around.
It does not look too complicated. I just have to find a general approach how to calculate the centroid of a set of objects. Maybe this requires that (similar to the distance function with the hierarchical cluster) the user needs to supply a utility function. But I'll try to avoid that.
Nevertheless, for objects to be clustered using the K-Means algorithm, they need to fulfill certain prerequisites. For one they need to be representable in vector-space.
I still need to investigate a bit on this.
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