Class MedianLinkage
- java.lang.Object
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- ch.usi.inf.sape.hac.agglomeration.MedianLinkage
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- All Implemented Interfaces:
AgglomerationMethod
public final class MedianLinkage extends java.lang.Object implements AgglomerationMethod
The "median", "weighted centroid", "weighted center of mass distance", "Gower", or "Weighted Pair-Group Method using Centroids (WPGMC)" method is a geometric approach. The size of the clusters is assumed to be equal and the position of the new centroid is always between the two old centroids. This method preserves the importance of a small cluster when it is merged with a large cluster. [The data analysis handbook. By Ildiko E. Frank, Roberto Todeschini] Can produce a dendrogram that is not monotonic (it can have so called inversions, which are hard to interpret). This occurs when the distance from the union of two clusters, r and s, to a third cluster is less than the distance between r and s. Used only for Euclidean distance! The distance between two clusters is the Euclidean distance between their weighted centroids. The general form of the Lance-Williams matrix-update formula: d[(i,j),k] = ai*d[i,k] + aj*d[j,k] + b*d[i,j] + g*|d[i,k]-d[j,k]| For the "median" method: ai = 0.5 aj = 0.5 b = -0.25 g = 0 Thus: d[(i,j),k] = 0.5*d[i,k] + 0.5*d[j,k] - 0.25*d[i,j]
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Constructor Summary
Constructors Constructor Description MedianLinkage()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description double
computeDissimilarity(double dik, double djk, double dij, int ci, int cj, int ck)
Compute the dissimilarity between the newly formed cluster (i,j) and the existing cluster k.java.lang.String
toString()
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Method Detail
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computeDissimilarity
public double computeDissimilarity(double dik, double djk, double dij, int ci, int cj, int ck)
Description copied from interface:AgglomerationMethod
Compute the dissimilarity between the newly formed cluster (i,j) and the existing cluster k.- Specified by:
computeDissimilarity
in interfaceAgglomerationMethod
- Parameters:
dik
- dissimilarity between clusters i and kdjk
- dissimilarity between clusters j and kdij
- dissimilarity between clusters i and jci
- cardinality of cluster icj
- cardinality of cluster jck
- cardinality of cluster k- Returns:
- dissimilarity between cluster (i,j) and cluster k.
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toString
public java.lang.String toString()
- Overrides:
toString
in classjava.lang.Object
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