Description Usage Arguments Details Value Author(s) References Examples

This function runs the simple k-medoid algorithm proposed by Budiaji and Leisch (2019).

1 | ```
skm(distdata, ncluster, seeding = 20, iterate = 10)
``` |

`distdata` |
A distance matrix ( |

`ncluster` |
A number of clusters. |

`seeding` |
A number of seedings to run the algorithm
( |

`iterate` |
A number of iterations for each seeding
( |

The simple k-medoids, which sets a set of medoids as the cluster centers, adapts the simple and fast k-medoid algoritm. The best practice to run the simple and fast k-medoid is by running the algorithm several times with different random seeding options.

Function returns a list of components:

`cluster`

is the clustering memberships result.

`medoid`

is the id medoids.

`minimum_distance`

is the distance of all objects to their cluster
medoid.

Weksi Budiaji

Contact: budiaji@untirta.ac.id

W. Budiaji, and F. Leisch. 2019. Simple K-Medoids Partitioning Algorithm for Mixed Variable Data. Algorithms Vol 12(9) 177

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