Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/jeremygelb/geocmeans

An R package to perform Spatial Fuzzy C-means
https://github.com/jeremygelb/geocmeans

clustering cmeans fuzzy-classification-algorithms r spatial-analysis spatial-fuzzy-cmeans unsupervised-learning

Last synced: 3 days ago
JSON representation

An R package to perform Spatial Fuzzy C-means

Awesome Lists containing this project

README 

        



# geocmeans 



## An R package to perform Spatial Fuzzy C-means.



[![R-CMD-check](https://github.com/JeremyGelb/geocmeans/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/JeremyGelb/geocmeans/actions/workflows/R-CMD-check.yaml)

[![](https://img.shields.io/badge/devel%20version-0.3.4-green.svg)](https://github.com/JeremyGelb/geocmeans)

[![](https://www.r-pkg.org/badges/version/geocmeans?color=blue)](https://cran.r-project.org/package=geocmeans)

[![](http://cranlogs.r-pkg.org/badges/grand-total/geocmeans?color=blue)](https://cran.r-project.org/package=geocmeans)

[![Codecov test

coverage](https://codecov.io/gh/JeremyGelb/geocmeans/branch/master/graph/badge.svg)](https://app.codecov.io/gh/JeremyGelb/geocmeans?branch=master)



The website of the package is available

[here](https://jeremygelb.github.io/geocmeans/)



## Breaking news



Here we are! We are moving from `maptools`, `sp`, `rgeos`, `raster` and

`rgdal` to `sf`, `terra` and `tmap`. All the functions and the

documentation were modified accordingly. If you spot an error or a bug,

please open an issue on github.



## Installation



The stable version of `geocmeans` is available on CRAN. You can install

it with the command below.



    install.packages("geocmeans")



You can install a development version of the `geocmeans` package using

the command below.



    remotes::install_github(repo = "JeremyGelb/geocmeans", build_vignettes = TRUE, force = TRUE)



## Authors



Jeremy Gelb, Laboratoire d’Équité Environnemental INRS (CANADA), Email:



## Contributors



Philippe Apparicio, Laboratoire d’Équité Environnemental INRS (CANADA),

Email: 



## About the package



Provides functions to apply Spatial Fuzzy c-means Algorithm, visualize

and interpret results. This method is well suited when the user wants to

analyze data with a fuzzy clustering algorithm and to account for the

spatial dimension of the dataset. In addition, indexes for measuring the

spatial consistency and classification quality are proposed. The

algorithms were developed first for brain imagery as described in the

articles of [Cai and

al. 2007](https://doi.org/10.1016/j.patcog.2006.07.011) and [Zaho and

al. 2013](https://doi.org/10.1016/j.dsp.2012.09.016). [Gelb and

Apparicio](https://doi.org/10.4000/cybergeo.36414) proposed to apply the

method to perform a socio-residential and environmental taxonomy in Lyon

(France). The methods can be applied to dataframes or to rasters.



#### Fuzzy classification algorithms



Four Fuzzy classification algorithms are proposed :



- FCM: Fuzzy C-Means, with the function `CMeans`

- GFCM: Generalized Fuzzy C-Means, with the function `GFCMeans`

- SFCM: Spatial Fuzzy C-Means, with the function `SFCMeans`

- SGFCM: Spatial Generalized Fuzzy C-Means, with the function

  `SGFCMeans`



Each function return a membership matrix, the data used for the

classification (scaled if required) and the centers of the clusters.



For each algorithm, it is possible to calculate a “robust version” and

to add a noise group (used to catch outliers). See the parameters

`robust` and `noise_cluser` in the documentation for more details.



#### Parameter selections



The algorithms available require different parameters to be fixed by the

user. The function `selectParameters` is a useful tool to compare the

results of different combinations of parameters. A multicore version,

`selectParameters.mc`, using a plan from the package `future` is also

available to speed up the calculus.



#### Classification quality



Many indices of classification quality can be calculated with the

function `calcqualityIndexes`:



- *Silhouette.index*: the silhouette index (`fclust::SIL.F`)

- *Partition.entropy*: the partition entropy index (`fclust::PE`)

- *Partition.coeff*: the partition entropy coefficient (`fclust::PC`)

- *Modified.partition.coeff*: the modified partition entropy coefficient

  (`fclust::MPC`)

- *XieBeni.index*: the Xie and Beni index (`fclust::XB`)

- *FukuyamaSugeno.index*: the Fukuyama and Sugeno index

  (`geocmeans::calcFukuyamaSugeno`)

- *DavidBoudlin.index*: the David-Bouldin index

  (`geocmeans::calcDavidBouldin`)

- *CalinskiHarabasz.index*: the Calinski-Harabasz index

  (`geocmeans::calcCalinskiHarabasz`)

- *GD43.index* and *GD53.index*: two version of the generalized Dunn

  index (`geocmeans::calcGD43` and `calcGD53`)

- *Negentropy.index*: the Negentropy Increment index

  (`geocmeans::calcNegentropyI`)

- *Explained.inertia*: the percentage of total inertia explained by the

  solution



#### Classification consistency



To assess the stability of the obtained clusters, a function for

bootstrap validation is proposed: `boot_group_validation`. The results

can be used to verify if the obtained clusters are stable and how much

their centres vary.



#### Reproductibility



Clustering methods like CMeans depend on the initial centers selected.

In `geocmeans`, they are selected randomly, and two runs of the

functions can yield different results. To facilitate the

reproductibility of the results, the main functions of the package

(`CMeans`, `GFCMeans`, `SFCMeans`, `SGFCMeans`, `selectParameters`,

`selectParameters.mc`) have a `seed` parameter. It can be set by the

user to ensure that the results of the functions are exactly the same.



#### Interpretation



Several functions are also available to facilitate the interpretation of

the classification:



- summary statistics for each cluster: `summarizeClusters` (also

  accessible with the generic function `summary`)

- spider charts: `spiderPlots`

- violin plots: `violinPlots`

- maps of the membership matrix: `mapClusters` (support polygon, points

  and polylines)



There is also a shiny app that can be used to go deeper in the result

interpretation. It requires the packages `shiny`, `leaflet`, `bslib`,

`plotly`, `shinyWidgets`, `car`.



![Alt

Text](https://raw.githubusercontent.com/JeremyGelb/geocmeans/master/.github/gif/app_viz.gif)



#### Spatial diagnostic



Several spatial indices can be calculated to have a better spatial

understanding of the obtained clusters, like the global or local Moran I

calculated on the membership values, or the join-count-test on the most

likely group for each observation. ELSA and Fuzzy ELSA statistics can

also be calculated to identify areas with high or low multidimensional

spatial autocorrelation in the membership values. See functions

`spConsistency`, `calcELSA`, `calcFuzzyELSA` and `spatialDiag`.



We proposed an index to quantify the spatial inconsistency of a

classification ([Gelb and

Apparicio](https://doi.org/10.4000/cybergeo.36414)). If in a

classification close observations tend to belong to the same group, then

the value of the index is close to 0. If the index is close to 1, then

the belonging to groups is randomly distributed in space. A value higher

than one can happen in the case of negative spatial autocorrelation. The

index is described in the vignette `adjustinconsistency`. The function

`spatialDiag` does a complete spatial diagnostic of the membership

matrix resulting from a classification.



## Examples



Detailed examples are given in the vignette `introduction`



    vignette("introduction","geocmeans")



## Testing



If you would like to install and run the unit tests interactively,

include `INSTALL_opts = "--install-tests"` in the installation code.



    remotes::install_github(repo = "JeremyGelb/geocmeans", build_vignettes = TRUE, force = TRUE, INSTALL_opts = "--install-tests")

    testthat::test_package("geocmeans", reporter = "stop")



## Contribute



To contribute to `geocmeans`, please follow these

[guidelines](https://github.com/JeremyGelb/geocmeans/blob/master/CONTRIBUTING.md).



Please note that the `geocmeans` project is released with a [Contributor

Code of

Conduct](https://github.com/JeremyGelb/geocmeans/blob/master/CONDUCT.md).

By contributing to this project, you agree to abide by its terms.



## License



`geocmeans` version 0.3.4 is licensed under [GPL2

License](https://github.com/JeremyGelb/geocmeans/blob/master/LICENSE.txt).