Spatial Clustering via the Cross Entropy Method

• Nishanthi Raveendran ^[1] ; G. Y. Sofronov ; David Bulger ^[1]
  1. [1] Department of Mathematics and Statistics
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 406-409
• Idioma: inglés
• Enlaces
  • Texto Completo Libro
• Resumen

  • 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.