Spatial clustering via the Cross Entropy Method

Nishanthi Raveendran; Georgy Sofronov; David Bulger

Spatial clustering via the Cross Entropy Method

Nishanthi Raveendran^*, Georgy Sofronov, David Bulger

^*Corresponding author for this work

Department of Mathematics and Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 35th International Workshop on Statistical Modelling
Editors	Itziar Irigoien, Dae-Jin Lee, Joaquín Martínez-Minaya, María Xosé Rodríguez- Álvarez
Publisher	University of the Basque Country
Pages	397-400
Number of pages	4
ISBN (Electronic)	9788413192673
Publication status	Published - 2020
Event	International Workshop on Statistical Modelling (35th : 2020) - Bilbao, Spain Duration: 20 Jul 2020 → 24 Jul 2020 Conference number: 35th https://wp.bcamath.org/iwsm2020/

Conference

Conference	International Workshop on Statistical Modelling (35th : 2020)
Abbreviated title	IWSM2020
Country	Spain
City	Bilbao
Period	20/07/20 → 24/07/20
Internet address	https://wp.bcamath.org/iwsm2020/

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Cite this

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title = "Spatial clustering via the Cross Entropy Method",

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.",

author = "Nishanthi Raveendran and Georgy Sofronov and David Bulger",

year = "2020",

language = "English",

pages = "397--400",

editor = "Itziar Irigoien and Dae-Jin Lee and Joaqu{\'i}n Mart{\'i}nez-Minaya and {Rodr{\'i}guez- {\'A}lvarez}, {Mar{\'i}a Xos{\'e}}",

booktitle = "Proceedings of the 35th International Workshop on Statistical Modelling",

publisher = "University of the Basque Country",

}

Raveendran, N , Sofronov, G & Bulger, D 2020, Spatial clustering via the Cross Entropy Method. in I Irigoien, D-J Lee, J Martínez-Minaya & MX Rodríguez- Álvarez (eds), Proceedings of the 35th International Workshop on Statistical Modelling. University of the Basque Country, pp. 397-400, International Workshop on Statistical Modelling (35th : 2020), Bilbao, Spain, 20/07/20.

Spatial clustering via the Cross Entropy Method. / Raveendran, Nishanthi ; Sofronov, Georgy ; Bulger, David.

Proceedings of the 35th International Workshop on Statistical Modelling. ed. / Itziar Irigoien; Dae-Jin Lee; Joaquín Martínez-Minaya; María Xosé Rodríguez- Álvarez. University of the Basque Country, 2020. p. 397-400.

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

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AU - Sofronov, Georgy

AU - Bulger, David

PY - 2020

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N2 - 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.

AB - 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.

M3 - Conference proceeding contribution

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BT - Proceedings of the 35th International Workshop on Statistical Modelling

A2 - Irigoien, Itziar

A2 - Lee, Dae-Jin

A2 - Martínez-Minaya, Joaquín

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