Minimal k for x-Means?

Muhammed_Fatih_

Dear community, 

my question looks like the following: Does x-means always take the minimal given k as optimum? 

I tried X-Means between the interval k-min=2 and k-max=60 as well as with k-min=20 and k-max=60 on my data. The x-means model gives me the minimal number of k (in the first time k=2 and in the second time k=20) every time. Is it normal that x-Means always picks the minimal number of k? 



Best regards!

mantanz

If possible please share your xml and let me know the number of examples in your data set.

The situation you stated can happen if you don't have too many examples for clustering, or they are simply too similar to one another so the X-means always resorts to the simplest clustering scheme.
In such case it is better to normalize the data beforehand. This will ensure all the attributes arrives at the same scale before the algorithm is applied.
For e.g. attribute1 has data range 0-100 and attribute2 has vector range 0-1. Now in this case attribute1 gets more weightage than attribute2. But if you apply normalise both attributes will covert to 0-1 scale.

Rapidminer Operator to be used : "Normalize"