cluster (version 1.14.4)

dissimilarity.object: 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 (as in the book, e.g. p.54): [object Object],[object Object],[object Object],[object Object],[object Object],[object Object].

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.