cluster_algorithm {GSgalgoR} | R Documentation

## Wrapper function to perform partition around medioids (PAM) for GalgoR

### Description

In `GSgalgoR`, the partition around medioids (PAM) algorithm is the default clustering process used under the evolutionary process.

### Usage

    cluster_algorithm(c, k)

### Arguments

`c` | a dissimilarity matrix object of type `'dist'`
`k` | positive integer specifying the number of clusters, less than the number of observations

### Details

The function runs the `pam` function of the `'cluster'` package with options `cluster.only =TRUE`, `diss = TRUE`, `do.swap=TRUE`, `keep.diss=FALSE`, `keep.data = FALSE`, `pamonce= 2`

### Value

Returns a `'list'` with the value `'$cluster'` which contains the cluster assignment of each of the samples evaluated

### References

- Reynolds, A., Richards, G., de la Iglesia, B. and Rayward-Smith, V. (1992) Clustering rules: A comparison of partitioning and hierarchical clustering algorithms; Journal of Mathematical Modelling and Algorithms 5, 475–504. 10.1007/s10852-005-9022-1.

- Erich Schubert and Peter J. Rousseeuw (2019) Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms; Preprint, (https://arxiv.org/abs/1810.05691).

### Examples

    # load example dataset
    require(iC10TrainingData)
    require(pamr)
    data(train.Exp)

    calculate_distance <- select_distance(distancetype = "pearson")
    Dist <- calculate_distance(train.Exp)
    k <- 4
    Pam <- cluster_algorithm(Dist, k)
    table(Pam$cluster)

[Package GSgalgoR version 1.2.1 Index]