Description Usage Arguments Details Value See Also

Evaluation on the varaince of a clustering model using squared Euclidean distances, based on distance matrix and cluster membership.

1 2 3 4 5 6 |

`dist.obj` |
a ‘dist’ object as produced by |

`clusters` |
a vector with cluster memberships. |

`k` |
numeric, the upper bound of the number of clusters to compute.
DEFAULT: |

`hclust.obj` |
a ‘hclust’ object, generated by |

`hclust.FUN` |
a function, to generate a hierarchical clustering.
Ignored with |

`hclust.FUN.MoreArgs` |
a list, containing arguments that are passed to |

Clustering Sum-of-Squares for clustering evaluation.

`css`

returns a ‘css’ object,
which is a list containing the following components

k | number of clusters |

wss | within-cluster sum-of-squares `k` |

totwss | total within-cluster sum-of-square |

totbss | total between-cluster sum-of-square |

tss | total sum of squares of the data |

, and with an attribute ‘meta’ that contains the input components

dist.obj | (the input) distance matrix |

clusters | (the input) cluster membership |

`css.hclust`

returns a ‘css.multi’ object,
which is a data.frame containing the following columns

k | number of clusters |

ev | explained variance given `k` |

totbss | total between-cluster sum-of-square |

tss | total sum of squares of the data |

, and with an attribute ‘meta’ that contains

cmethod | the clustering method |

dist.obj | (the input) distance matrix |

k | (the input) number of clusters |

clusters | the `hclust' object that is either by input or computed by default |

`elbow`

for "elbow" plot using ‘css.multi’ object

GMD documentation built on May 29, 2017, 10:41 a.m.

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