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Non-hierarchical clustering: CLARA

Source: R/nhclu_clara.R
nhclu_clara.Rd

This function performs non-hierarchical clustering based on dissimilarity using partitioning around medoids, implemented via the Clustering Large Applications (CLARA) algorithm.

Usage

nhclu_clara(
  dissimilarity,
  index = names(dissimilarity)[3],
  seed = NULL,
  n_clust = c(1, 2, 3),
  maxiter = 0,
  initializer = "LAB",
  fasttol = 1,
  numsamples = 5,
  sampling = 0.25,
  independent = FALSE,
  algorithm_in_output = TRUE
)

Arguments

dissimilarity

The output object from dissimilarity() or similarity_to_dissimilarity(), or a dist object. If a data.frame is used, the first two columns should represent pairs of sites (or any pair of nodes), and the subsequent column(s) should contain the dissimilarity indices.

index

The name or number of the dissimilarity column to use. By default, the third column name of dissimilarity is used.

seed

A value for the random number generator (set to NULL for random initialization by default).

n_clust

An integer vector or a single integer specifying the desired number(s) of clusters.

maxiter

An integer defining the maximum number of iterations.

initializer

A character string, either "BUILD" (used in the classic PAM algorithm) or "LAB" (Linear Approximate BUILD).

fasttol

A positive numeric value defining the tolerance for fast swapping behavior. Defaults to 1.

numsamples

A positive integer specifying the number of samples to draw.

sampling

A positive numeric value defining the sampling rate.

independent

A boolean indicating whether the previous medoids are excluded in the next sample. Defaults to FALSE.

algorithm_in_output

A boolean indicating whether the original output of fastclara should be included in the output. Defaults to TRUE (see Value).

Value

A list of class bioregion.clusters with five components:

  1. name: A character string containing the name of the algorithm.

  2. args: A list of input arguments as provided by the user.

  3. inputs: A list of characteristics of the clustering process.

  4. algorithm: A list of all objects associated with the clustering procedure, such as original cluster objects (only if algorithm_in_output = TRUE).

  5. clusters: A data.frame containing the clustering results.

If algorithm_in_output = TRUE, the algorithm slot includes the output of fastclara.

Details

Based on fastkmedoids package (fastclara).

References

Schubert E & Rousseeuw PJ (2019) Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms. Similarity Search and Applications 11807, 171-187.

See also

For more details illustrated with a practical example, see the vignette: https://biorgeo.github.io/bioregion/articles/a4_2_non_hierarchical_clustering.html.

Associated functions: nhclu_clarans nhclu_dbscan nhclu_kmeans nhclu_pam nhclu_affprop

Author

Pierre Denelle (pierre.denelle@gmail.com)
Boris Leroy (leroy.boris@gmail.com)
Maxime Lenormand (maxime.lenormand@inrae.fr)

Examples

comat <- matrix(sample(0:1000, size = 500, replace = TRUE, prob = 1/1:1001),
20, 25)
rownames(comat) <- paste0("Site",1:20)
colnames(comat) <- paste0("Species",1:25)

dissim <- dissimilarity(comat, metric = "all")

clust1 <- nhclu_clara(dissim, index = "Simpson", n_clust = 5)

bioregionalization_metrics(clust1, dissimilarity = dissim,
eval_metric = "pc_distance")
#> Computing similarity-based metrics...
#>   - pc_distance OK
#> Partition metrics:
#>  - 1  partition(s) evaluated
#>  - Range of clusters explored: from  5  to  5 
#>  - Requested metric(s):  pc_distance 
#>  - Metric summary:
#>      pc_distance
#> Min    0.4639792
#> Mean   0.4639792
#> Max    0.4639792
#> 
#> Access the data.frame of metrics with your_object$evaluation_df