Significant locations in auxiliary data as seeds for typical use cases of point clustering
Keywords: web mapping, cluster initialization, cluster preprocessing, significant locations, population density
Abstract. Random greedy clustering and grid-based clustering are highly susceptible by their initial parameters. When used for point data clustering in maps they often change the apparent distribution of the underlying data. We propose a process that uses precomputed weighted seed points for the initialization of clusters, for example from local maxima in population density data. Exemplary results from the clustering of a dataset of petrol stations are presented.