during a cluster analysis procedure, how would I approach finding an appropriate number of clusters within my data? I've been experimenting with kmeans a little doing the following:
- run kmeans (with m clusters) on my feature set n times, n times because I wanted to try to overcome the limitations of random outcomes given the nature of the algorithm
- pick the "majority vote" out of the n "cluster votes" in order to choose the appropriate cluster membership
- iterate, i.e. repeat the procedure over a range of assumed amount of clusters within the data
What are alternatives to the approach sketched above?
Another issue is the fact, that I have "ordered, categorical" (ordinal) data in my dataset. I know that this might be a problem with kmeans. What are my alternatives algorithm-wise?
Thanks in advance, Andi