nag_mv_cluster_indicator (g03ejc) computes a cluster indicator variable from the results of
nag_mv_hierar_cluster_analysis (g03ecc).
Given a distance or dissimilarity matrix for
objects, cluster analysis aims to group the
objects into a number of more or less homogeneous groups or clusters. With agglomerative clustering methods (see
nag_mv_hierar_cluster_analysis (g03ecc)), 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.
nag_mv_cluster_indicator (g03ejc) takes the information from the tree and produces the clusters that exist at a given distance. This is equivalent to taking the dendrogram (see
nag_mv_dendrogram (g03ehc)) and drawing a line across at a given distance to produce clusters.
As an alternative to giving the distance at which clusters are required, you can specify the number of clusters required and nag_mv_cluster_indicator (g03ejc) will compute the corresponding distance. However, it may not be possible to compute the number of clusters required due to ties in the distance matrix.
- NE_2_INT_ARG_GT
-
On entry, while . These arguments must satisfy .
- NE_CLUSTER
-
The precise number of clusters requested is not possible because of
tied clustering distances. The actual number of clusters produced is .
- NE_INCOMP_ARRAYS
-
Arrays
cd and
dord are not compatible.
- NE_INT_ARG_LT
-
On entry, .
Constraint: .
- NE_INTERNAL_ERROR
-
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 for
assistance.
- NE_NOT_INCREASING
-
The sequence
cd is not increasing:
,
.
- NE_REAL_INT
-
On entry, , .
Constraint: and .
- NW_2_INT
-
On exit, , .
Trivial solution returned.
- NW_INT
-
On exit, .
Trivial solution returned.
- NW_REAL_REALARR
-
On entry, , .
Trivial solution returned.
The accuracy will depend upon the accuracy of the distances in
cd and
dord (see
nag_mv_hierar_cluster_analysis (g03ecc)).
A fixed number of clusters can be found using the non-hierarchical method used in
nag_mv_kmeans_cluster_analysis (g03efc).
Data consisting of three variables on five objects are input. Euclidean squared distances are computed using
nag_mv_distance_mat (g03eac) and median clustering performed using
nag_mv_hierar_cluster_analysis (g03ecc). A dendrogram is produced by
nag_mv_dendrogram (g03ehc) and printed.
nag_mv_cluster_indicator (g03ejc) finds two clusters and the results are printed.