Spatial clustering via the Cross Entropy Method

Nishanthi Raveendran*, Georgy Sofronov, David Bulger

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 35th International Workshop on Statistical Modelling
EditorsItziar Irigoien, Dae-Jin Lee, Joaquín Martínez-Minaya, María Xosé Rodríguez- Álvarez
PublisherUniversity of the Basque Country
Pages397-400
Number of pages4
ISBN (Electronic)9788413192673
Publication statusPublished - 2020
EventInternational Workshop on Statistical Modelling (35th : 2020) - Bilbao, Spain
Duration: 20 Jul 202024 Jul 2020
Conference number: 35th
https://wp.bcamath.org/iwsm2020/

Conference

ConferenceInternational Workshop on Statistical Modelling (35th : 2020)
Abbreviated titleIWSM2020
CountrySpain
CityBilbao
Period20/07/2024/07/20
Internet address

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