This function will return the individual stability score *ST* and the global score *STglobal* using either the K-means or K-medoids algorithm and four different clustering indices: Calinski-Harabasz, Silhouette, Dunn or Davies-Bouldin.

1 | ```
ClusterStability(dat, k, replicate, type)
``` |

`dat` |
the input dataset: either a matrix or a dataframe. |

`k` |
the number of classes for the K-means or K-medoids algorithm (default=3). |

`replicate` |
the number of replicates to perform (default=1000). |

`type` |
the algorithm used in the partitioning: either 'kmeans' or 'kmedoids' algorithm (default=kmeans). |

Returns the individual (*ST*) and global (*ST_global*) stability scores for the four clustering indices: Calinski-Harabasz (*ch*), Silhouette (*sil*), Dunn (*dunn*) or Davies-Bouldin (*db*).

1 2 3 | ```
## Calculates the stability scores of individual objects of the Iris dataset
## using K-means, 100 replicates (random starts) and k=3
ClusterStability(dat=iris[1:4],k=3,replicate=100,type='kmeans');
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.