My question is, how I can make a cluster analysis from spatial - temporal and high dimensional data? my purpose is to find subspace clusters that can show patterns in the space and in the time. over here space mean a geographic position, so I should use autocorrelation law (also knowns like Tobler law or the first law from geography). is this right?, first I make a transformation from time to frequency through Wavelets transform from every variable (because all variables have time and geographic position related) and after that, taking that coefficients and applying one subspace clustering algorithm for temporal high-dimensional clustering. once I have the temporal clusters I try to find a spatial "cluster" trough regionalization between temporal clusters.
Thanks in Advance any light.
I understand that you use the toblers law as an interpretation of the spatial correlation (regionalization). Its not clear what the final application would be but, a few verification steps i would do in such circumstances would be: to check if the all(150) variables are all corresponding to the same scale in space and time, affected by the same kind of autocorrelation (stationarity) which can simplify the problems in few cases. And finally also has to understand what features or patterns are to be extracted and how they are characterized. Check this out: http://www.geokernels.org/pages/modern_indexpag.html
Hope it helped !
Cheers Ravi