I have a set of sea surface temperature (SST) monthly data and I want to apply some cluster methodology to detect regions with similar SST patterns. I have a set of monthly data files running from 1985 to 2009 and want to apply clustering to each month as a first step.
Each file contains gridded data for 358416 points where approximately 50% are land and are marked with a 99.99 value that will be NA. Data format is:
lon lat sst
-10.042 44.979 12.38
-9.998 44.979 12.69
-9.954 44.979 12.90
-9.910 44.979 12.90
-9.866 44.979 12.54
-9.822 44.979 12.37
-9.778 44.979 12.37
-9.734 44.979 12.51
-9.690 44.979 12.39
-9.646 44.979 12.36
I have tried CLARA clustering method and got some apparently nice results but it also seems to me that is just smoothing (grouping) isolines. Then I am not sure this is the best clustering method to analyse spatial data.
Is there any other clustering method devoted to this type of datasets? Some reference would be good to start reading.
Thanks in advance.
Best Answer
There is different approach for scalable clustering, divide and conquer approach, parallel clustering and incremental one. This is for general approach after you can use normal clustering methods. There a good method of clustering i really appreciate is DBSCAN (Density-Based Spatial Clustering of Applications with Noise) it is one of the most used clustering algorithm.