fuzzy_k_means {paar}

Fuzzy k-means clustering

Description

Performs a vectorized fuzzy k-means clustering, this procedure it is not spatial. The function is almost a wrapper of the function cmeans from the package e1071. Is intended to be used when 'KM-sPC' procedure is not possible because data set has only 1 variable.

Usage

fuzzy_k_means(
  data,
  variables,
  number_cluster = 3:5,
  fuzzyness = 1.2,
  distance = "euclidean"
)

Arguments

data

sf object

variables

variables to use for clustering, if missing, all numeric variables will be used

number_cluster

numeric vector with number of final clusters

fuzzyness

A number greater than 1 giving the degree of fuzzification.

distance

character Must be one of the following: If "euclidean", the mean square error, if "manhattan", the mean absolute error is computed. Abbreviations are also accepted.

Value

a list with classification results and indices to select best number of clusters.

Examples

library(sf)
data(wheat, package = 'paar')

# Transform the data.frame into a sf object
wheat_sf <- st_as_sf(wheat,
                     coords = c('x', 'y'),
                     crs = 32720)

# Run the fuzzy_k_means function
fuzzy_k_means_results <- fuzzy_k_means(wheat_sf,
                               variables = 'Tg',
                               number_cluster = 2:4)

# Print the summaryResults
fuzzy_k_means_results$summaryResults

# Print the indices
fuzzy_k_means_results$indices

# Print the cluster
head(fuzzy_k_means_results$cluster, 5)

# Combine the results in a single object
wheat_clustered <- cbind(wheat_sf, fuzzy_k_means_results$cluster)

# Plot the results
plot(wheat_clustered[, "Cluster_2"])