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.
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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: 20 or the number of observations (if less than 20). |
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 | k within-cluster sum-of-squares |
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
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