# dissimilarity.object

##### Dissimilarity Matrix Object

Objects of class `"dissimilarity"`

representing the dissimilarity
matrix of a dataset.

- Keywords
- cluster

##### 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:

the number of observations in the dataset.

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".

optionally, contains the labels, if any, of the observations of the dataset.

optionally, if a dissimilarity could not be computed, because of too many missing values for some observations of the dataset.

when a mixed metric was used, the types for each variable as one-letter codes (as in the book, e.g. p.54):

- A
Asymmetric binary

- S
Symmetric binary

- N
Nominal (factor)

- O
Ordinal (ordered factor)

- I
Interval scaled (numeric)

- T
raTio to be log transformed (positive numeric)

##### 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

*Documentation reproduced from package cluster, version 2.0.7-1, License: GPL (>= 2)*