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 contributionpeer-review

    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
    Country/TerritorySpain
    CityBilbao
    Period20/07/2024/07/20
    Internet address

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