Description Value GENERATION METHODS See Also

Objects of class `"dissimilarity"`

representing the dissimilarity
matrix of a dataset.

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(i,j)*
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): - 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)
. |

`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.

The `"dissimilarity"`

class has methods for the following generic
functions: `print`

, `summary`

.

`daisy`

, `dist`

,
`pam`

, `clara`

, `fanny`

,
`agnes`

, `diana`

.

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