diana { cluster } | R Documentation |

Computes a divisive hierarchical clustering of the dataset returning an object of class < code >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 < code >diss argument. 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 (< code >NAs) < em >are allowed. In case of a dissimilarity matrix, < code >x is typically the output of < code >daisy or < code >dist. Also a vector of length n*(n-1)/2 is allowed (where n is the number of observations), and will be interpreted in the same way as the output of the above-mentioned functions. Missing values (NAs) are < em >not allowed. |

`diss` |
logical flag: if TRUE (default for < code >dist or < code >dissimilarity objects), then < code >x will be considered as a dissimilarity matrix. If FALSE, then < code >x will be considered as a matrix of observations by variables. |

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

`stand` |
logical; if true, the measurements in < code >x are standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's mean absolute deviation. If < code >x is already a dissimilarity matrix, then this argument will be ignored. |

`stop.at.k` |
logical or integer, < code >FALSE by default.
Otherwise must be integer, say |

`keep.diss, keep.data` |
logicals indicating if the dissimilarities and/or input data < code >x should be kept in the result. Setting these to < code >FALSE can give much smaller results and hence even save memory allocation < em >time. |

`trace.lev` |
integer specifying a trace level for printing diagnostics during the algorithm. Default < code >0 does not print anything; higher values print increasingly more. |

< code >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, < code >diana provides (a) the divisive coefficient (see < code >diana.object) which measures the amount of clustering structure found; and (b) the banner, a novel graphical display (see < code >plot.diana).

The < code >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 < code >"diana" representing the clustering; this class has methods for the following generic functions: < code >print, < code >summary, < code >plot.

Further, the class < code >"diana" inherits from < code >"twins". Therefore, the generic function < code >pltree can be used on a < code >diana object, and < code >as.hclust and < code >as.dendrogram methods are available.

A legitimate < code >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 < code >order, but containing observation labels instead of observation numbers. This component is only available if the original observations were labelled. |

`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 < code >merge describes the split at step n-i of
the clustering. If a number |

`diss` |
an object of class < code >"dissimilarity", representing the total dissimilarity matrix of the dataset. |

`data` |
a matrix containing the original or standardized measurements, depending on the < code >stand option of the function < code >agnes. If a dissimilarity matrix was given as input structure, then this component is not available. |

< code >agnes also for background and references; < code >cutree (and < code >as.hclust) for grouping extraction; < code >daisy, < code >dist, < code >plot.diana, < code >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)

[ Package *cluster* version 2.0.7-1 Index ]