On model based clustering in a spatial data mining context

Abstract

In this paper we present the finite mixture models approach to clustering of high dimensional data. The mixture resolving approach to cluster analysis has been addressed in a number of different ways; the underlying assumption is that the patterns to be clustered are drawn from one of several distributions, and the goal is to identify the parameters of each and (perhaps) their number. Finite mixture models allows a flexible approach to the statistical modeling of phenomena characterized by unobserved heterogeneity in different fields of applications. In this analysis we consider the model based clustering on mixture models and compare it with the classical k-means approach. The application regards some aspects of the 218 Municipalities of the region Friuli Venezia Giulia in North-Eastern Italy with data based on the Italian population 2011 Census. © 2013 Springer-Verlag Berlin Heidelberg.