agnes {cluster} | R Documentation |

Computes agglomerative hierarchical clustering of the dataset.

```
agnes(x, diss = inherits(x, "dist"), metric = "euclidean",
stand = FALSE, method = "average", par.method,
keep.diss = n < 100, keep.data = !diss, trace.lev = 0)
```

`x` |
data matrix or data frame, or dissimilarity matrix, 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 (NAs) are allowed. 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.
The currently available options are |

`stand` |
logical flag: if TRUE, then the measurements in |

`method` |
character string defining the clustering method. The six methods
implemented are
The default is |

`par.method` |
If |

`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 |

`agnes`

is fully described in chapter 5 of Kaufman and Rousseeuw (1990).
Compared to other agglomerative clustering methods such as `hclust`

,
`agnes`

has the following features: (a) it yields the
agglomerative coefficient (see `agnes.object`

)
which measures the amount of clustering structure found; and (b)
apart from the usual tree it also provides the banner, a novel
graphical display (see `plot.agnes`

).

The `agnes`

-algorithm constructs a hierarchy of clusterings.

At first, each observation is a small cluster by itself. Clusters are
merged until only one large cluster remains which contains all the
observations. At each stage the two *nearest* clusters are combined
to form one larger cluster.

For `method="average"`

, the distance between two clusters is the
average of the dissimilarities between the points in one cluster and the
points in the other cluster.

In `method="single"`

, we use the smallest dissimilarity between a
point in the first cluster and a point in the second cluster (nearest
neighbor method).

When `method="complete"`

, we use the largest dissimilarity
between a point in the first cluster and a point in the second cluster
(furthest neighbor method).

The `method = "flexible"`

allows (and requires) more details:
The Lance-Williams formula specifies how dissimilarities are
computed when clusters are agglomerated (equation (32) in K&R(1990),
p.237). If clusters `C_1`

and `C_2`

are agglomerated into a
new cluster, the dissimilarity between their union and another
cluster `Q`

is given by

```
D(C_1 \cup C_2, Q) = \alpha_1 * D(C_1, Q) + \alpha_2 * D(C_2, Q) +
\beta * D(C_1,C_2) + \gamma * |D(C_1, Q) - D(C_2, Q)|,
```

where the four coefficients `(\alpha_1, \alpha_2, \beta, \gamma)`

are specified by the vector `par.method`

, either directly as vector of
length 4, or (more conveniently) if `par.method`

is of length 1,
say `= \alpha`

, `par.method`

is extended to
give the “Flexible Strategy” (K&R(1990), p.236 f) with
Lance-Williams coefficients ```
(\alpha_1 = \alpha_2 = \alpha, \beta =
1 - 2\alpha, \gamma=0)
```

.

Also, if `length(par.method) == 3`

, `\gamma = 0`

is set.

**Care** and expertise is probably needed when using `method = "flexible"`

particularly for the case when `par.method`

is specified of
longer length than one. Since cluster version 2.0, choices
leading to invalid `merge`

structures now signal an error (from
the C code already).
The *weighted average* (`method="weighted"`

) is the same as
`method="flexible", par.method = 0.5`

. Further,
`method= "single"`

is equivalent to `method="flexible", par.method = c(.5,.5,0,-.5)`

, and
`method="complete"`

is equivalent to `method="flexible", par.method = c(.5,.5,0,+.5)`

.

The `method = "gaverage"`

is a generalization of `"average"`

, aka
“flexible UPGMA” method, and is (a generalization of the approach)
detailed in Belbin et al. (1992). As `"flexible"`

, it uses the
Lance-Williams formula above for dissimilarity updating, but with
`\alpha_1`

and `\alpha_2`

not constant, but *proportional* to
the *sizes* `n_1`

and `n_2`

of the clusters `C_1`

and
`C_2`

respectively, i.e,

`\alpha_j = \alpha'_j \frac{n_1}{n_1+n_2},`

where `\alpha'_1`

, `\alpha'_2`

are determined from `par.method`

,
either directly as `(\alpha_1, \alpha_2, \beta, \gamma)`

or
`(\alpha_1, \alpha_2, \beta)`

with `\gamma = 0`

, or (less flexibly,
but more conveniently) as follows:

Belbin et al proposed “flexible beta”, i.e. the user would only
specify `\beta`

(as `par.method`

), sensibly in

`-1 \leq \beta < 1,`

and `\beta`

determines `\alpha'_1`

and `\alpha'_2`

as

`\alpha'_j = 1 - \beta,`

and `\gamma = 0`

.

This `\beta`

may be specified by `par.method`

(as length 1 vector),
and if `par.method`

is not specified, a default value of -0.1 is used,
as Belbin et al recommend taking a `\beta`

value around -0.1 as a general
agglomerative hierarchical clustering strategy.

Note that `method = "gaverage", par.method = 0`

(or ```
par.method =
c(1,1,0,0)
```

) is equivalent to the `agnes()`

default method `"average"`

.

an object of class `"agnes"`

(which extends `"twins"`

)
representing the clustering. See `agnes.object`

for
details, and methods applicable.

Cluster analysis divides a dataset into groups (clusters) of observations that are similar to each other.

- Hierarchical methods
like

`agnes`

,`diana`

, and`mona`

construct a hierarchy of clusterings, with the number of clusters ranging from one to the number of observations.- Partitioning methods
like

`pam`

,`clara`

, and`fanny`

require that the number of clusters be given by the user.

Method `"gaverage"`

has been contributed by Pierre Roudier, Landcare
Research, New Zealand.

Kaufman, L. and Rousseeuw, P.J. (1990). (=: “K&R(1990)”)
*Finding Groups in Data: An Introduction to Cluster Analysis*.
Wiley, New York.

Anja Struyf, Mia Hubert and Peter J. Rousseeuw (1996)
Clustering in an Object-Oriented Environment.
*Journal of Statistical Software* **1**.
doi:10.18637/jss.v001.i04

Struyf, A., Hubert, M. and Rousseeuw, P.J. (1997). Integrating
Robust Clustering Techniques in S-PLUS,
*Computational Statistics and Data Analysis*, **26**, 17–37.

Lance, G.N., and W.T. Williams (1966).
A General Theory of Classifactory Sorting Strategies, I. Hierarchical
Systems.
*Computer J.* **9**, 373–380.

Belbin, L., Faith, D.P. and Milligan, G.W. (1992). A Comparison of
Two Approaches to Beta-Flexible Clustering.
*Multivariate Behavioral Research*, **27**, 417–433.

`agnes.object`

, `daisy`

, `diana`

,
`dist`

, `hclust`

, `plot.agnes`

,
`twins.object`

.

```
data(votes.repub)
agn1 <- agnes(votes.repub, metric = "manhattan", stand = TRUE)
agn1
plot(agn1)
op <- par(mfrow=c(2,2))
agn2 <- agnes(daisy(votes.repub), diss = TRUE, method = "complete")
plot(agn2)
## alpha = 0.625 ==> beta = -1/4 is "recommended" by some
agnS <- agnes(votes.repub, method = "flexible", par.meth = 0.625)
plot(agnS)
par(op)
## "show" equivalence of three "flexible" special cases
d.vr <- daisy(votes.repub)
a.wgt <- agnes(d.vr, method = "weighted")
a.sing <- agnes(d.vr, method = "single")
a.comp <- agnes(d.vr, method = "complete")
iC <- -(6:7) # not using 'call' and 'method' for comparisons
stopifnot(
all.equal(a.wgt [iC], agnes(d.vr, method="flexible", par.method = 0.5)[iC]) ,
all.equal(a.sing[iC], agnes(d.vr, method="flex", par.method= c(.5,.5,0, -.5))[iC]),
all.equal(a.comp[iC], agnes(d.vr, method="flex", par.method= c(.5,.5,0, +.5))[iC]))
## Exploring the dendrogram structure
(d2 <- as.dendrogram(agn2)) # two main branches
d2[[1]] # the first branch
d2[[2]] # the 2nd one { 8 + 42 = 50 }
d2[[1]][[1]]# first sub-branch of branch 1 .. and shorter form
identical(d2[[c(1,1)]],
d2[[1]][[1]])
## a "textual picture" of the dendrogram :
str(d2)
data(agriculture)
## Plot similar to Figure 7 in ref
## Not run: plot(agnes(agriculture), ask = TRUE)
data(animals)
aa.a <- agnes(animals) # default method = "average"
aa.ga <- agnes(animals, method = "gaverage")
op <- par(mfcol=1:2, mgp=c(1.5, 0.6, 0), mar=c(.1+ c(4,3,2,1)),
cex.main=0.8)
plot(aa.a, which.plot = 2)
plot(aa.ga, which.plot = 2)
par(op)
## Show how "gaverage" is a "generalized average":
aa.ga.0 <- agnes(animals, method = "gaverage", par.method = 0)
stopifnot(all.equal(aa.ga.0[iC], aa.a[iC]))
```

[Package *cluster* version 2.1.4 Index]