Q:

got stuck in Cluster analysis, way forward?

I have a situation, where I need to classify items into groups (lets say 6). When I ran k-means 90% of my data fall in 1 group remaining 10% fall in other groups. What's next step?
In order to further group the data, I have taken the 90% data group and once again I ran k-means.This time I have 15 new groups within this new dataset. But now again 76% fell in one group remaining in 14 groups? How to deal in such situation?

A:

There are lots of options with cluster analysis. You can choose different measures of distance and linkage. You could use hierarchical clusters instead of k-means. There are even fuzzy clusters. You have the R tag so I'm guessing you are using that.
On this page there is a large list of clustering functions in R. Each will have many options. Which have you tried?
Within the k means sort of idea, for instance, in pam you can alter the distance metric (use Manhattan instead of Euclidean) The 'flexclust` package offers lots of options.

