On the Problem of Clustering Spatial Big Data

Different motivation are related with the analysis of Spatial Big Data (SBD). Google Earth, Google Maps, Navigation, location-based service allow to obtain a great amount of geo-referenced data. Often spatial datasets exceed the capacity of current computing systems to manage, process, or analyze the data with reasonable effort. Considering SBD history methodology as Dataintensive Computing and Data Mining techniques have been useful. In this context the problem regards the analysis of of high frequency spatial data. In this paper we present an approach to clustering of high dimensional data which allows a flexible approach to the statistical modeling of phenomena characterized by unobserved heterogeneity. We consider the MDBSCAN and compare it with the classical k-means approach. The applications concern a synthetic data set and a data set of satellite images.