HierarchicalClustering {less} | R Documentation |
Wrapper R6 Class of stats::hclust function that can be used for LESSRegressor and LESSClassifier
R6 Class of HierarchicalClustering
less::BaseEstimator
-> HierarchicalClustering
new()
Creates a new instance of R6 Class of HierarchicalClustering
HierarchicalClustering$new(linkage = "ward.D2", n_clusters = 8)
linkage
the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC) (defaults to ward.D2).
n_clusters
the number of clusters (defaults to 8).
hc <- HierarchicalClustering$new() hc <- HierarchicalClustering$new(n_clusters = 10) hc <- HierarchicalClustering$new(n_clusters = 10, linkage = "complete")
fit()
Perform hierarchical clustering on a data matrix.
HierarchicalClustering$fit(X)
X
numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns).
Fitted R6 class of HierarchicalClustering() that has 'labels' attribute
data(abalone) hc <- HierarchicalClustering$new() hc$fit(abalone[1:100,])
get_cluster_centers()
Auxiliary function returning the cluster centers
HierarchicalClustering$get_cluster_centers()
print(hc$get_cluster_centers())
get_labels()
Auxiliary function returning a vector of integers (from 1:k) indicating the cluster to which each point is allocated.
HierarchicalClustering$get_labels()
print(hc$get_labels())
clone()
The objects of this class are cloneable with this method.
HierarchicalClustering$clone(deep = FALSE)
deep
Whether to make a deep clone.
## ------------------------------------------------
## Method `HierarchicalClustering$new`
## ------------------------------------------------
hc <- HierarchicalClustering$new()
hc <- HierarchicalClustering$new(n_clusters = 10)
hc <- HierarchicalClustering$new(n_clusters = 10, linkage = "complete")
## ------------------------------------------------
## Method `HierarchicalClustering$fit`
## ------------------------------------------------
data(abalone)
hc <- HierarchicalClustering$new()
hc$fit(abalone[1:100,])
## ------------------------------------------------
## Method `HierarchicalClustering$get_cluster_centers`
## ------------------------------------------------
print(hc$get_cluster_centers())
## ------------------------------------------------
## Method `HierarchicalClustering$get_labels`
## ------------------------------------------------
print(hc$get_labels())