Scikit Learn: KMeans Clustering 3D data over a time period (dimentionality reduction?)
I don't have enough reputation to ask questions in comment for clarification before answering it, so I'm going to do both here.
Here are the things that would help answering this question for now:
Can you post part of the process_set.hpc?
What's its format? Is it a numpy array? Is it a Pandas dataframe?
What's the value of cclasses?
And now the answer:
First of all, k-means algorithm is able to find clusters in any n-dimensional data. If n is too big, it is better to use PCA but for n=3 that wouldn't necessarily add any value.
The second thing that looks suspicious to me is that in the documentation for kmeans in scikit-learn, there is no compute_labels option, as seen here. However, that option exists for MiniBatchKMeans as seen here.
Also, if you make your data in the form of a pandas dataframe (if it is not already so), things would be much easier to track and you won't have to reattach the timing information to your data afterwards.
I may be able to give you a more thorough answer if I know a bit more about the format of the data.
Good luck!