Partitioning around medoids

```
Cluster_Medoids(
data,
clusters,
distance_metric = "euclidean",
minkowski_p = 1,
threads = 1,
swap_phase = TRUE,
fuzzy = FALSE,
verbose = FALSE,
seed = 1
)
```

- data
matrix or data frame. The data parameter can be also a dissimilarity matrix, where the main diagonal equals 0.0 and the number of rows equals the number of columns

- clusters
the number of clusters

- distance_metric
a string specifying the distance method. One of,

*euclidean*,*manhattan*,*chebyshev*,*canberra*,*braycurtis*,*pearson_correlation*,*simple_matching_coefficient*,*minkowski*,*hamming*,*jaccard_coefficient*,*Rao_coefficient*,*mahalanobis*,*cosine*- minkowski_p
a numeric value specifying the minkowski parameter in case that distance_metric = "minkowski"

- threads
an integer specifying the number of cores to run in parallel

- swap_phase
either TRUE or FALSE. If TRUE then both phases ('build' and 'swap') will take place. The 'swap_phase' is considered more computationally intensive.

- fuzzy
either TRUE or FALSE. If TRUE, then probabilities for each cluster will be returned based on the distance between observations and medoids

- verbose
either TRUE or FALSE, indicating whether progress is printed during clustering

- seed
`r lifecycle::badge("deprecated")` `seed` (integer value for random number generator (RNG)) is no longer supported and will be removed in version 1.4.0

a list with the following attributes: medoids, medoid_indices, best_dissimilarity, dissimilarity_matrix, clusters, fuzzy_probs (if fuzzy = TRUE), silhouette_matrix, clustering_stats

Due to the fact that I didn't have access to the book 'Finding Groups in Data, Kaufman and Rousseeuw, 1990' (which includes the exact algorithm) I implemented the 'Cluster_Medoids' function based on the paper 'Clustering in an Object-Oriented Environment' (see 'References'). Therefore, the 'Cluster_Medoids' function is an approximate implementation and not an exact one. Furthermore, in comparison to k-means clustering, the function 'Cluster_Medoids' is more robust, because it minimizes the sum of unsquared dissimilarities. Moreover, it doesn't need initial guesses for the cluster centers.

Anja Struyf, Mia Hubert, Peter J. Rousseeuw, (Feb. 1997), Clustering in an Object-Oriented Environment, Journal of Statistical Software, Vol 1, Issue 4

```
data(dietary_survey_IBS)
dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]
dat = center_scale(dat)
cm = Cluster_Medoids(dat, clusters = 3, distance_metric = 'euclidean', swap_phase = TRUE)
#> Warning: The `seed` argument of `Cluster_Medoids()` is deprecated as of ClusterR 1.2.6.
#> ℹ The 'seed' parameter will be removed in version 1.4.0
```