Question

I haven’t had much experience with machine learning or clustering, so I’m at a bit of a loss as to how to approach this problem. My data of interest consists of 4 columns, one of which is just an id. The other 3 contain numerical data, values >= 0. The clustering I need is actually quite straightforward, and I could do it by hand, but it will get less clear later on so I want to start out with the right sort of process. I need 6 clusters, which depend on the 3 columns (call them A, B and C) as follows:

A    B    C        Cluster
---- ---- -------- -------
0    0    0        0
0    0    >0       1
0    >0   <=B      2
0    >0   >B       3
>0   any  <=(A+B)  4
>0   any  >(A+B)   5

At this stage, these clusters will give an insight to the data to inform further analysis.

Since I’m quite new to this, I haven’t yet learned enough about the various algorithms which do clustering, so I don’t really know where to start. Could anyone suggest an appropriate model to use, or a few that I can research.

Answer 1

This does not look like clustering to me.

Instead, I figure you want a simple decision tree classification.

It should already be available in Rapidminer.