diana | R Documentation |

Computes a divisive hierarchical clustering of the dataset
returning an object of class `diana`

.

```
diana(x, diss = inherits(x, "dist"), metric = "euclidean", stand = FALSE,
stop.at.k = FALSE,
keep.diss = n < 100, keep.data = !diss, trace.lev = 0)
```

`x` |
data matrix or data frame, or dissimilarity matrix or object,
depending on the value of the In case of a matrix or data frame, each row corresponds to an observation,
and each column corresponds to a variable. All variables must be numeric.
Missing values ( In case of a dissimilarity matrix, |

`diss` |
logical flag: if TRUE (default for |

`metric` |
character string specifying the metric to be used for calculating
dissimilarities between observations. |

`stand` |
logical; if true, the measurements in |

`stop.at.k` |
logical or integer, |

`keep.diss, keep.data` |
logicals indicating if the dissimilarities
and/or input data |

`trace.lev` |
integer specifying a trace level for printing
diagnostics during the algorithm. Default |

`diana`

is fully described in chapter 6 of Kaufman and Rousseeuw (1990).
It is probably unique in computing a divisive hierarchy, whereas most
other software for hierarchical clustering is agglomerative.
Moreover, `diana`

provides (a) the divisive coefficient
(see `diana.object`

) which measures the amount of clustering structure
found; and (b) the banner, a novel graphical display
(see `plot.diana`

).

The `diana`

-algorithm constructs a hierarchy of clusterings,
starting with one large
cluster containing all n observations. Clusters are divided until each cluster
contains only a single observation.

At each stage, the cluster with the largest diameter is selected.
(The diameter of a cluster is the largest dissimilarity between any
two of its observations.)

To divide the selected cluster, the algorithm first looks for its most
disparate observation (i.e., which has the largest average dissimilarity to the
other observations of the selected cluster). This observation initiates the
"splinter group". In subsequent steps, the algorithm reassigns observations
that are closer to the "splinter group" than to the "old party". The result
is a division of the selected cluster into two new clusters.

an object of class `"diana"`

representing the clustering;
this class has methods for the following generic functions:
`print`

, `summary`

, `plot`

.

Further, the class `"diana"`

inherits from
`"twins"`

. Therefore, the generic function `pltree`

can be
used on a `diana`

object, and `as.hclust`

and
`as.dendrogram`

methods are available.

A legitimate `diana`

object is a list with the following components:

`order` |
a vector giving a permutation of the original observations to allow for plotting, in the sense that the branches of a clustering tree will not cross. |

`order.lab` |
a vector similar to |

`height` |
a vector with the diameters of the clusters prior to splitting. |

`dc` |
the divisive coefficient, measuring the clustering structure of the
dataset. For each observation i, denote by |

`merge` |
an (n-1) by 2 matrix, where n is the number of
observations. Row i of |

`diss` |
an object of class |

`data` |
a matrix containing the original or standardized measurements, depending
on the |

`agnes`

also for background and references;
`cutree`

(and `as.hclust`

) for grouping
extraction; `daisy`

, `dist`

,
`plot.diana`

, `twins.object`

.

```
data(votes.repub)
dv <- diana(votes.repub, metric = "manhattan", stand = TRUE)
print(dv)
plot(dv)
## Cut into 2 groups:
dv2 <- cutree(as.hclust(dv), k = 2)
table(dv2) # 8 and 42 group members
rownames(votes.repub)[dv2 == 1]
## For two groups, does the metric matter ?
dv0 <- diana(votes.repub, stand = TRUE) # default: Euclidean
dv.2 <- cutree(as.hclust(dv0), k = 2)
table(dv2 == dv.2)## identical group assignments
str(as.dendrogram(dv0)) # {via as.dendrogram.twins() method}
data(agriculture)
## Plot similar to Figure 8 in ref
## Not run: plot(diana(agriculture), ask = TRUE)
```

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