R: Clustering large applications

Clara_Medoids {ClusterR} | R Documentation

## Clustering large applications

### Description

Clustering large applications

### Usage

    Clara_Medoids(
      data,
      clusters,
      samples,
      sample_size,
      distance_metric = "euclidean",
      minkowski_p = 1,
      threads = 1,
      swap_phase = TRUE,
      fuzzy = FALSE,
      verbose = FALSE,
      seed = 1
    )

### Arguments

`data` | matrix or data frame
`clusters` | the number of clusters
`samples` | number of samples to draw from the data set
`sample_size` | fraction of data to draw in each sample iteration. It should be a float number greater than 0.0 and less or equal to 1.0
`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. Openmp will be utilized to parallelize the number of the different sample draws
`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` | integer value for random number generator (RNG)

### Details

The Clara_Medoids function is implemented in the same way as the 'clara' (clustering large applications) algorithm (Kaufman and Rousseeuw(1990)). In the 'Clara_Medoids' the 'Cluster_Medoids' function will be applied to each sample draw.

### Value

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

### Author(s)

Lampros Mouselimis

### References

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

### Examples

    data(dietary_survey_IBS)

    dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

    dat = center_scale(dat)

    clm = Clara_Medoids(dat, clusters = 3, samples = 5, sample_size = 0.2, swap_phase = TRUE)

[Package ClusterR version 1.2.2 Index]