Description Usage Arguments Details Value Author(s) References Examples

This function runs the increasing number of clusters in the k-medoids algorithm proposed by Yu et. al. (2018).

1 | ```
inckmed(distdata, ncluster, iterate = 10, alpha = 1)
``` |

`distdata` |
A distance matrix ( |

`ncluster` |
A number of clusters. |

`iterate` |
A number of iterations for the clustering algorithm. |

`alpha` |
A stretch factor to determine the range of initial medoid
selection ( |

This algorithm is claimed to manage with the weakness of the
simple and fast-kmedoids (`fastkmed`

). The origin of the
algorithm is a centroid-based algorithm by applying the Euclidean distance.
Then, Bbecause the function is a medoid-based algorithm, the object mean
(centroid) and variance are redefined into medoid and deviation, respectively.

The `alpha`

argument is a stretch factor, i.e. a constant defined by
the user. It is applied to determine a set of medoid candidates. The medoid
candidates are calculated by
*O_c = *{*X_i*| *σ_i ≤q α σ,
i = 1, 2, …, n* },
where *σ_i* is the average deviation of object *i*, and
*σ* is the average deviation of the data set. They are computed by

*σ = √{\frac{1}{n-1} ∑_{i=1}^n d(O_i, v_1)}*

*σ_i = √{\frac{1}{n-1} ∑_{i=1}^n d(O_i, O_j)}*

where *n* is the number of objects, *O_i* is the object *i*,
and *v_1* is the most centrally located object.

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

Yu, D., Liu, G., Guo, M., Liu, X., 2018. An improved K-medoids algorithm based on step increasing and optimizing medoids. Expert Systems with Applications 92, pp. 464-473.

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