daltoolbox (version 1.3.767)

cluster_hclust: Hierarchical clustering

Description

Agglomerative hierarchical clustering using stats::hclust.

Usage

cluster_hclust(
  k = 2,
  h = NULL,
  method = c("ward.D2", "ward.D", "single", "complete", "average", "mcquitty", "median",
    "centroid"),
  dist = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
  scale = TRUE
)

Value

returns a hierarchical clustering object.

Arguments

k

number of clusters to cut the tree (default 2)

h

height to cut the tree (optional; if provided, overrides k)

method

linkage method passed to stats::hclust (default "ward.D2")

dist

distance method passed to stats::dist (default "euclidean")

scale

logical; whether to scale data before distance (default TRUE)

Details

Computes a distance matrix (optionally after scaling) and builds a dendrogram. Clusters are obtained by cutting the tree with k (number of clusters) or h (height).

The base clusterer() uses wcss as a generic default, but cluster_hclust() specializes that choice because a hierarchical partition is obtained from a dendrogram and is better judged through separation/cohesion indices than through a centroid-style fallback alone.

Default evaluation in cluster_hclust() is:

• main metric: metric_silhouette()

• internal evaluation: silhouette, davies_bouldin, calinski_harabasz

• external evaluation: entropy, purity, adjusted_rand_index

The general wcss fallback is not kept as the main metric because the method is not fitted by directly minimizing that criterion.

References

Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis.

Examples

data(iris)
model <- cluster_hclust(k = 3)
model <- fit(model, iris[,1:4])
clu <- cluster(model, iris[,1:4])
table(clu)