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Non-hierarchical clustering: CLARANS

Source: R/nhclu_clarans.R
nhclu_clarans.Rd

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

Usage

nhclu_clarans(
  dissimilarity,
  index = names(dissimilarity)[3],
  seed = NULL,
  n_clust = c(1, 2, 3),
  numlocal = 2,
  maxneighbor = 0.025,
  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 (NULL for random initialization by default).

n_clust

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

numlocal

An integer defining the number of local searches to perform.

maxneighbor

A positive numeric value defining the maximum number of neighbors to consider for each local search.

algorithm_in_output

A boolean indicating whether the original output of fastclarans 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 fastclarans.

Details

Based on fastkmedoids package (fastclarans).

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_clara 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_clarans(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     0.544898
#> Mean    0.544898
#> Max     0.544898
#> 
#> Access the data.frame of metrics with your_object$evaluation_df