Record Class HierarchicalClustering
java.lang.Object
java.lang.Record
smile.clustering.HierarchicalClustering
- Record Components:
tree- the hierarchical cluster tree. An n-1 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 >= nthen the merge was with the cluster formed at the (earlier) stage j-n of the algorithm.height- the clustering height. A set of n-1 non-decreasing real values, which are the value of the criterion associated with the clustering method for the particular agglomeration.
- All Implemented Interfaces:
Serializable
public record HierarchicalClustering(int[][] tree, double[] height)
extends Record
implements 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:
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Constructor Summary
ConstructorsConstructorDescriptionHierarchicalClustering(int[][] tree, double[] height) Creates an instance of aHierarchicalClusteringrecord class. -
Method Summary
Modifier and TypeMethodDescriptionfinal booleanIndicates whether some other object is "equal to" this one.static HierarchicalClusteringFits the Agglomerative Hierarchical Clustering with given linkage method, which includes proximity matrix.final inthashCode()Returns a hash code value for this object.double[]height()Returns the value of theheightrecord component.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.final StringtoString()Returns a string representation of this record class.int[][]tree()Returns the value of thetreerecord component.
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Constructor Details
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HierarchicalClustering
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Method Details
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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.
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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.
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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.
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toString
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hashCode
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equals
Indicates whether some other object is "equal to" this one. The objects are equal if the other object is of the same class and if all the record components are equal. All components in this record class are compared withObjects::equals(Object,Object). -
tree
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height
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