Daniela Stojanova: Global and Local Spatial Autocorrelation in Predictive Clustering Trees

Spatial autocorrelation is the correlation among data values, which is strictly due to the relative location proximity of the objects that the data refer to. This statistical property clearly indicates a violation of the assumption of observation independence - a pre-condition assumed by most of the data mining and statistical models. Inappropriate treatment of data with spatial dependencies could obfuscate important insights when spatial autocorrelation is ignored:.

We propose a data mining method that explicitly considers autocorrelation when building the models.
The proposed approach combines the possibility of capturing both global and local effects (common to top-down model tree learners) and detecting / dealing with positive spatial autocorrelation (common to spatial statistical methods). As a consequence, the discovered models adapt to local properties of the data, providing at the same time spatially smoothed predictions.

Thursday, 24 February 2011, 11:00, MPŠ predavalinica