Package smile.clustering
Class HierarchicalClustering
java.lang.Object
smile.clustering.HierarchicalClustering
 All Implemented Interfaces:
Serializable
Agglomerative Hierarchical Clustering. Hierarchical agglomerative clustering
seeks to build a hierarchy of clusters in a bottom up approach: each
observation starts in its own cluster, and pairs of clusters are merged as
one moves up the hierarchy. The results of hierarchical clustering are
usually presented in a dendrogram.
In general, the merges are determined in a greedy manner. In order to decide which clusters should be combined, a measure of dissimilarity between sets of observations is required. In most methods of hierarchical clustering, this is achieved by use of an appropriate metric, and a linkage criteria which specifies the dissimilarity of sets as a function of the pairwise distances of observations in the sets.
Hierarchical clustering has the distinct advantage that any valid measure of distance can be used. In fact, the observations themselves are not required: all that is used is a matrix of distances.
References
 David Eppstein. Fast hierarchical clustering and other applications of dynamic closest pairs. SODA 1998.
 See Also:

Constructor Summary

Method Summary
Modifier and TypeMethodDescriptionstatic HierarchicalClustering
Fits the Agglomerative Hierarchical Clustering with given linkage method, which includes proximity matrix.double[]
height()
Returns a set of n1 nondecreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration.int[]
partition
(double h) Cuts a tree into several groups by specifying the cut height.int[]
partition
(int k) Cuts a tree into several groups by specifying the desired number.int[][]
tree()
Returns an n1 by 2 matrix of which row i describes the merging of clusters at step i of the clustering.

Constructor Details

HierarchicalClustering
public HierarchicalClustering(int[][] tree, double[] height) Constructor. Parameters:
tree
 an n1 by 2 matrix of which row i describes the merging of clusters at step i of the clustering.height
 the clustering height.


Method Details

fit
Fits the Agglomerative Hierarchical Clustering with given linkage method, which includes proximity matrix. Parameters:
linkage
 a linkage method to merge clusters. The linkage object includes the proximity matrix of data. Returns:
 the model.

tree
public int[][] tree()Returns an n1 by 2 matrix of which row i describes the merging of clusters at step i of the clustering. If an element j in the row is less than n, then observation j was merged at this stage. Ifj >= n
then the merge was with the cluster formed at the (earlier) stage jn of the algorithm. Returns:
 the merge tree.

height
public double[] height()Returns a set of n1 nondecreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration. Returns:
 the tree node height.

partition
public int[] partition(int k) Cuts a tree into several groups by specifying the desired number. Parameters:
k
 the number of clusters. Returns:
 the cluster label of each sample.

partition
public int[] partition(double h) Cuts a tree into several groups by specifying the cut height. Parameters:
h
 the cut height. Returns:
 the cluster label of each sample.
