Resumen de Spatial Clustering via the Cross Entropy Method
Nishanthi Raveendran, G. Y. Sofronov, David Bulger
Spatial clustering is an important component of spatial data analysis which aims to identify the number of clusters and their boundaries. Applications include epidemiology, criminology and many others. In this study, we focus on identifying homogeneous clusters in binary data, which indicate the presence or absence of a certain plant species observed over a two-dimensional lattice. To solve this clustering problem, we propose to combine the Cross Entropy method with Voronoi tessellation to estimate the boundaries of such domains. Our results illustrate that the proposed algorithm is e ective in identifying homogeneous clusters in spatial binary data.
