Class WeightedAverageLinkage
- java.lang.Object
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- ch.usi.inf.sape.hac.agglomeration.WeightedAverageLinkage
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- All Implemented Interfaces:
AgglomerationMethod
public final class WeightedAverageLinkage extends Object implements AgglomerationMethod
The "weighted average", "McQuitty", or "Weighted Pair-Group Method using Arithmetic averages, or WPGMA)" method. Average linkage where the sizes of the clusters are assumed to be equal. This method, similar to "Median", weights small and large clusters equally. [The data analysis handbook. By Ildiko E. Frank, Roberto Todeschini] 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 "McQuitty" method: ai = 0.5 aj = 0.5 b = 0 g = 0 Thus: d[(i,j),k] = 0.5*d[i,k] + 0.5*d[j,k]
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Constructor Summary
Constructors Constructor Description WeightedAverageLinkage()
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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.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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