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Fuzzy k-means clustering (non-spatial)

Source: R/fuzzy_k_means.R
fuzzy_k_means.Rd

Performs fuzzy k-means clustering on tabular data (non-spatial). This function is a lightweight wrapper around e1071::cmeans, providing a vectorized workflow and clustering quality indices.

It is primarily intended as a fallback method when spatial clustering (e.g., kmspc) cannot be applied, such as when only one variable is available.

Usage

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

Arguments

data

an sf object with point geometries

variables

character vector with variable names used for clustering. If missing, all numeric variables in data are used.

number_cluster

numeric vector indicating the number of clusters to evaluate (e.g., 3:5)

fuzzyness

numeric value greater than 1 controlling the degree of fuzziness in clustering (see e1071::cmeans)

distance

character distance metric for clustering. One of "euclidean" or "manhattan" (abbreviations allowed)

Value

A list with:

cluster

data.frame with cluster assignments for each evaluated number of clusters

indices

data.frame with clustering validity indices

summaryResults

data.frame with clustering metrics

Details

Missing values are removed prior to clustering. Observations with missing values are reintroduced in the output with NA cluster assignments.

Clustering is performed for each value in number_cluster, and several indices are returned to assist in selecting the optimal number of clusters:

  • Xie-Beni index

  • Partition coefficient

  • Partition entropy

  • Summary index

See also

kmspc

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
#>   Clusters Iterations        SSDW
#> 1        2         20 0.020516877
#> 2        3         43 0.010596950
#> 3        4         34 0.006439882

# Print the indices
fuzzy_k_means_results$indices
#>   Num. Cluster     Xie Beni Partition Coefficient Entropy of Partition
#> 1            2 2.340529e-05             0.9709446           0.04784131
#> 2            3 2.407361e-05             0.9603949           0.06536385
#> 3            4 2.348571e-05             0.9582419           0.06906399
#>   Summary Index
#> 1      1.557271
#> 2      1.695319
#> 3      1.710485

# Print the cluster
head(fuzzy_k_means_results$cluster, 5)
#>      Cluster_2 Cluster_3 Cluster_4
#> [1,] "2"       "2"       "2"      
#> [2,] "2"       "2"       "2"      
#> [3,] "2"       "2"       "2"      
#> [4,] "2"       "2"       "2"      
#> [5,] "2"       "2"       "2"      

# 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"])