I have several data sets of frequency values (See Fig. 1 for an example).
I'm interested in those tighter clusters (marked by green rectangles) and am using hierarchical clustering in MATLAB (with unweighted average distance method) to separate them. (*)
The spread of these clusters increases with frequency (the standard deviation is positively correlated with the frequency, while the coefficient of variation is not).
So here's my question: Is there a way for the clustering to factor in this relationship, so that the average distance a point has to have to the points of the nearest cluster to be included in that cluster is also dependent on - for example - the mean of that cluster?
I'm thinking that the cutoff would have to be different for each point, but I don't think this is possible in this method. I would also be open to alternative clustering methods, but not k-means, because I don't want to specify the number of clusters in advance.
Also, if you have suggestions on rephrasing my question so that it may be more useful to others I would be grateful.
Thank you for your time!
(*) I'm following this example for the clustering procedure: http://www.mathworks.de/de/help/stats/examples/cluster-analysis.html