geocmeans: geocmeans: A package implementing methods for spatially...
In geocmeans: Implementing Methods for Spatial Fuzzy Unsupervised Classification
geocmeans R Documentation
geocmeans: A package implementing methods for spatially constrained c-means
algorithm
Description
The geocmeans package implements a modified c-means algorithm more suited to
work with spatial data (characterized by spatial autocorrelation). The
spatial information is introduced with a spatial weight matrix W (n * n)
where wij indicate the strength of the spatial relationship between the
observations i and j. It is recommended to use a matrix standardized by row
(so that the sum of each row is 1). More specifically, the spatial c-means
combine the euclidean distance of each observation in the data matrix X to
each center with the euclidean distance of the lagged version of X by W (WX).
A parameter alpha controls for the weight of the lagged matrix. If
alpha = 0, then the spatial c-means is equal to a classical c-means. If alpha
= 1, then the weights given to X and WX are equals. If alpha = 2, then the
weight of WX is twice the one of X and so on.
Several indices are provided to assess the quality of a classification on the
semantic and spatial dimensions. To explore results, a shiny app is also
available
geocmeans documentation built on Sept. 12, 2023, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| geocmeans | R Documentation |
geocmeans: A package implementing methods for spatially constrained c-means algorithm
Description
The geocmeans package implements a modified c-means algorithm more suited to work with spatial data (characterized by spatial autocorrelation). The spatial information is introduced with a spatial weight matrix W (n * n) where wij indicate the strength of the spatial relationship between the observations i and j. It is recommended to use a matrix standardized by row (so that the sum of each row is 1). More specifically, the spatial c-means combine the euclidean distance of each observation in the data matrix X to each center with the euclidean distance of the lagged version of X by W (WX). A parameter alpha controls for the weight of the lagged matrix. If alpha = 0, then the spatial c-means is equal to a classical c-means. If alpha = 1, then the weights given to X and WX are equals. If alpha = 2, then the weight of WX is twice the one of X and so on. Several indices are provided to assess the quality of a classification on the semantic and spatial dimensions. To explore results, a shiny app is also available
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.