# Cluster analysis in bounded data

I have 261 vectors with 9 attributes. Each attributes contains numbers between 0 and 1. I am not sure what the most appropriate clustering method for this kind of data is. Initially, I used the K-means algorithm but was reading about the drawbacks of K-means and found that K-means can fail when uniform data is used. This is explained in the following link. How to understand the drawbacks of K-means So, my questions are: what would be the best way to do a cluster analysis on this kind of data or how can I deal with it?. Also, where could I implement it (R, Python…)?

• What do you mean uniform data? If data are uniform there is no (nonrandom) clusters in it. Or are you saying distribution inside clusters is uniform? What is the shape of the clusters then? – ttnphns Jul 1 '15 at 8:14
• Maybe Alex is confusing bounded data with uniform data? Attributes being bounded between 0 and 1 will not cause k-means to fail. – Chris Jul 1 '15 at 18:13
• Yes, sorry. I wanted to say bounded. I was just thinking in two attributes (and not in all nine) and a question arises, what if my observations are highly concentrated in a certain range of values, how I can be sure or what can do to get the best possible clusters. – Alex Vega Jul 2 '15 at 0:29