I am currently trying to spatially cluster data that is ordered on a grid. Each point has x and y coordinates as well as a measurement value. These features come from a time series where I analyze each timestamp individually.
So far I've used hierarchical clustering with great success, the clusters are nearly always what I would expect. However, since I am interested in cluster development as well as the intra- and intercluster variance I cluster the data for every timestamp. This results in a problem since the specific cluster indices are not temporally consistent. What was cluster 2 in the last timestamp might be cluster 1 in the next which makes a time-based analysis much more difficult.
Is there a different clustering method that results in temporally stable cluster indices or does anyone know of a method to reliably track the cluster indices? I thought about tracking the movement of the centroids and assigning the previous cluster label to the nearest new centroid but this doesn't seem consistent as well since it strongly depends on the changes in the data.