This is the subroutine that automatically finds cluster assignments from given f and delta by testing various parameter settings and find the one that maximizes the silhouette.

1 2 | ```
FindClustersAuto(distm, f, delta, ac = 1, nclust = 2:10, f.cut = c(0.1,
0.2, 0.3))
``` |

`distm` |
the distance matrix |

`f` |
vector of local distance f(x). See the help of adpclust() for details. |

`delta` |
vector of minimal distances to higher ground delta(x). See the help of adpclust() for details. |

`ac` |
type of auto selection. The valid options are 1 and 2. See the help of adpclust() for details. |

`nclust` |
number of clusters to test. Either a single integer or a vector of integers. |

`f.cut` |
number between (0, 1) or numeric vector of numbers between (0, 1). Data points whose f values are larger than f.cut with large delta values are selected as centers. The default is c(0.1, 0.2, 0.3). See the help of FindCentersAutoV() for more details. |

list of four elements:

clusters Cluster assignments. A vector of the same length as the number of observations.

centers: Indices of the clustering centers.

silhouette: Silhouette score from the final clustering result.

nclust: Number of clusters.

Ethan Xu

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