NAG Library Function Document
nag_mv_hierar_cluster_analysis (g03ecc) performs hierarchical cluster analysis.
||nag_mv_hierar_cluster_analysis (Nag_ClusterMethod method,
const double d,
Given a distance or dissimilarity matrix for
objects (see nag_mv_distance_mat (g03eac)
), cluster analysis aims to group the
objects into a number of more or less homogeneous groups or clusters. With agglomerative clustering methods, a hierarchical tree is produced by starting with
clusters, each with a single object and then at each of
stages, merging two clusters to form a larger cluster, until all objects are in a single cluster. This process may be represented by a dendrogram (see nag_mv_dendrogram (g03ehc)
At each stage, the clusters that are nearest are merged, methods differ as to how the distance between the new cluster and other clusters are computed. For three clusters
be the number of objects in each cluster and let
be the distances between the clusters. Let clusters
be merged to give cluster
, then the distance from cluster
can be computed in the following ways:
- Single link or nearest neighbour: .
- Complete link or furthest neighbour: .
- Group average: .
- Centroid: .
- Median: .
- Minimum variance: .
If the clusters are numbered then, for convenience, if clusters and , , merge then the new cluster will be referred to as cluster . Information on the clustering history is given by the values of , and for each of the clustering steps. In order to produce a dendrogram, the ordering of the objects such that the clusters that merge are adjacent is required. This ordering is computed so that the first element is 1. The associated distances with this ordering are also computed.
Everitt B S (1974) Cluster Analysis Heinemann
Krzanowski W J (1990) Principles of Multivariate Analysis Oxford University Press
method – Nag_ClusterMethodInput
: indicates which clustering.
- Single link.
- Complete link.
- Group average.
- Minimum variance.
, , , , or .
n – IntegerInput
On entry: the number of objects, .
d – const doubleInput
On entry: the strictly lower triangle of the distance matrix. must be stored packed by rows, i.e., , must contain .
, for .
ilc – IntegerOutput
contains the number,
, of the cluster merged with cluster
, at step
iuc – IntegerOutput
On exit: contains the number, , of the cluster merged with cluster , , at step , for .
cd – doubleOutput
On exit: contains the distance , between clusters and , , merged at step , for .
iord[n] – IntegerOutput
On exit: the objects in dendrogram order.
dord[n] – doubleOutput
: the clustering distances corresponding to the order in iord
contains the distance at which cluster
contains the maximum distance.
fail – NagError *Input/Output
The NAG error argument (see Section 3.6
in the Essential Introduction).
6 Error Indicators and Warnings
Dynamic memory allocation failed.
On entry, argument method
had an illegal value.
A true dendrogram cannot be formed because the distances at which clusters
have merged are not increasing for all steps, i.e., for
some . This can occur for the and methods.
On entry, .
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG
On entry, .
Constraint: , for .
For methods other than or , slight rounding errors may occur in the calculations of the updated distances. These would not normally significantly affect the results, however there may be an effect if distances are (almost) equal.
If at a stage, two distances and , or and , are equal then clusters and will be merged rather than clusters and . For single link clustering this choice will only affect the order of the objects in the dendrogram. However, for other methods the choice of rather than may affect the shape of the dendrogram. If either of the distances or are affected by rounding errors then their equality, and hence the dendrogram, may be affected.
The dendrogram may be formed using nag_mv_dendrogram (g03ehc)
. Groupings based on the clusters formed at a given distance can be computed using nag_mv_cluster_indicator (g03ejc)
Data consisting of three variables on five objects are read in. Euclidean squared distances based on two variables are computed using nag_mv_distance_mat (g03eac)
, the objects are clustered using nag_mv_hierar_cluster_analysis (g03ecc) and the dendrogram computed using nag_mv_dendrogram (g03ehc)
. The dendrogram is then printed.
9.1 Program Text
Program Text (g03ecce.c)
9.2 Program Data
Program Data (g03ecce.d)
9.3 Program Results
Program Results (g03ecce.r)