Class CompleteLinkage
java.lang.Object
ch.usi.inf.sape.hac.agglomeration.CompleteLinkage
- All Implemented Interfaces:
AgglomerationMethod
The "complete", "maximum", "clique",
"furthest neighbor", or "furthest distance" method is a graph-based approach.
The distance between two clusters is calculated as the largest distance
between two objects in opposite clusters.
This method tends to produce well separated, small, compact spherical clusters.
The cluster space is dilated.
[The data analysis handbook. By Ildiko E. Frank, Roberto Todeschini]
This method tends to produce compact clusters. Outliers are given more weight with this method.
It is generally a good choice if the clusters are far apart in feature space, but not good if the data are noisy.
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptiondouble
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.toString()
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Constructor Details
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CompleteLinkage
public CompleteLinkage()
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Method Details
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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
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