# Randomly cluster a data set into K clusters.

### Description

For each observation (row) in 'x', one of K labels is randomly generated. By default, the probabilities of selecting each clustering label are equal, but this can be altered by specifying 'prob', a vector of probabilities for each cluster.

### Usage

1 | ```
random_clustering(x, K, prob = NULL)
``` |

### Arguments

`x` |
a matrix containing the data to cluster. The rows are the sample observations, and the columns are the features. |

`K` |
the number of clusters |

`prob` |
a vector of probabilities to generate each cluster label. If NULL, each cluster label has an equal chance of being selected. |

### Details

Random clustering is often utilized as a baseline comparison clustering against which other clustering algorithms are employed to identify structure within the data. Typically, comparisons are made in terms of proposed clustering assessment and evaluation methods as well as clustering similarity measures. For the former, a specified clustering evaluation method is computed for the considered clustering algorithms as well as random clustering. If the clusters determined by a considered clustering algorithm do not differ significantly from the random clustering, we might conclude that the found clusters are no better than naively choosing clustering labels for each observation at random. Likewise, a similarity measure can be computed to compare the clusterings from each of a considered clustering algorithm and a random clustering: if the clusterings are significantly similar, once again, we might conclude the clusters found via the considered clustering algorithm do not differ significantly from those found at random. In either case, the clusters are unlikely to provide meaningful results on which the user can better understand the inherent structure within the data.

### Value

a vector of clustering labels for each observation in 'x'.