dissimilarity.object: Dissimilarity Matrix Object

In cluster: "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.

Dissimilarity Matrix Object

Description

Objects of class "dissimilarity" representing the dissimilarity matrix of a dataset.

Value

The dissimilarity matrix is symmetric, and hence its lower triangle (column wise) is represented as a vector to save storage space. If the object, is called do, and n the number of observations, i.e., n <- attr(do, "Size"), then for i < j <= n, the dissimilarity between (row) i and j is do[n*(i-1) - i*(i-1)/2 + j-i]. The length of the vector is n*(n-1)/2, i.e., of order n^2.

"dissimilarity" objects also inherit from class dist and can use dist methods, in particular, as.matrix, such that d_{ij} from above is just as.matrix(do)[i,j].

The object has the following attributes:

Size	the number of observations in the dataset.
Metric	the metric used for calculating the dissimilarities. Possible values are "euclidean", "manhattan", "mixed" (if variables of different types were present in the dataset), and "unspecified".
Labels	optionally, contains the labels, if any, of the observations of the dataset.
NA.message	optionally, if a dissimilarity could not be computed, because of too many missing values for some observations of the dataset.
Types	when a mixed metric was used, the types for each variable as one-letter codes, see also type in daisy(): A: Asymmetric binary S: Symmetric binary N: Nominal (factor) O: Ordinal (ordered factor) I: Interval scaled, possibly after log transform "logratio" (numeric) T: raTio treated as ordered

GENERATION

daisy returns this class of objects. Also the functions pam, clara, fanny, agnes, and diana return a dissimilarity object, as one component of their return objects.

METHODS

The "dissimilarity" class has methods for the following generic functions: print, summary.

See Also

daisy, dist, pam, clara, fanny, agnes, diana.

cluster documentation built on Nov. 28, 2023, 1:07 a.m.