Clustering unrelated (with no correlation) data The objective of clustering analysis is to group data with similar characteristics in clusters, but in this case, I want to find the most unrelated data to group into clusters. In my particular case, I have 100 weather stations during one year with a 1hr interval, and I want to group the most similar weather stations(I performed this step with K-means using the correlation distance via MATLAB, but I can use R or Python). But now, I want to perform the inverse, i.e., group in the same cluster unrelated (with no correlation) weather stations .
Is this possible? If yes, how? Or, should I use others techniques to execute my idea?
The main idea is to use the groups to prove a concept often used in wind power designated as statistical power smoothing effect (pdf). Basically, there is a statistical power smoothing effect in the wind power data, if you considered data with different features (e.g., different weather conditions). I want to use this grouping to show if I carefully grouping my weather stations (that will be transformed in wind power), then I can minimize the fluctuations. So far I applied k-means algorithm to select the weather stations with the same behavior, and now I want to explore the opposite. Probably, the technique that I need is not clustering, but so far I search and nothing came up.
@Pere: Yes, I also expected what you mentioned. Can you provide some reference to understand how I can compute the correlation inverse distance?
@Dougal: I want diverse groups, as in the normal cluster.  To be honest the goal was to split the data in the same number of the "normal" clustering, in my case 9.
@ Pere: Thank you for the example. But I didn't achieve the expected results since, in my case, some clusters are very close to the ones obtained with the "normal" cluster. Probably it is better to try a trial and error test (adding/removing weather stations) to understand the weather stations that I should combine to smooth my signal. 
 A: To cluster the less similar points instead of the more similar, you just need to change the distance matrix in a way that the more different the points are, the lesser distance are given. A simple way to do this is just to use any decreasing function of the usual distance.
In your case your usual distance is probably 1-correlation (distance 0 for very correlated points and 1 for uncorrelated), so you can use as distance the inverse of 1-correlation or even 1+correlation.
I'll put an example of clustering the most different point using geographical distances - that is, clusters will have the most distant points instead of the closest neighbours.
I'll start with the distances between European cities found in http://www.mapcrow.info/european_travel_distance.html (in my code this matrix is the dataset "distsciutats").
distinv<-(1/distsciutats)
di<-as.dist(distinv)
fiti<-hclust(di,method="complete")
plot(fiti,main="clustering by inverse geographical distance")


You can see how the most distant cities are clustered together.
Just for comparison, I clustered the same cities using geographical distance, as usual.
 dc<-as.dist(distsciutats)
 fit<-hclust(dc,method="complete")
 plot(fit,main="clustering by geographical distance")


