Computes the distance between two clusters using Ward's method.
Namespace:
CenterSpace.NMath.StatsAssembly: NMathStats (in NMathStats.dll) Version: 3.4.0.0
Syntax
| C# |
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public static double WardLinkage( double Drp, double Drq, double Dpq, double Nr, double Np, double Nq ) |
| Visual Basic (Declaration) |
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Public Shared Function WardLinkage ( _ Drp As Double, _ Drq As Double, _ Dpq As Double, _ Nr As Double, _ Np As Double, _ Nq As Double _ ) As Double |
| Visual C++ |
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public: static double WardLinkage( double Drp, double Drq, double Dpq, double Nr, double Np, double Nq ) |
Parameters
- Drp
- Type: System..::.Double
The distance between R and P.
- Drq
- Type: System..::.Double
The distance between R and Q.
- Dpq
- Type: System..::.Double
The distance between P and Q.
- Nr
- Type: System..::.Double
The number of objects in R.
- Np
- Type: System..::.Double
The number of objects in P.
- Nq
- Type: System..::.Double
The number of objects in Q.
Return Value
The distance between R and P + Q.
Remarks
During cluster analysis when two groups P and Q are united, a linkage
function computes the distance between the new group P + Q and
another group R.
Ward's method uses an analysis of variance approach to evaluate the distances between clusters. The smaller the increase in the total within-group sum of squares as a result of joining two clusters, the "closer" they are. The within-group sum of squares of a cluster is defined as the sum of the squares of the distance between all objects in the cluster and the centroid of the cluster. Ward's method tends to produce compact groups of well-distributed size.
Ward's method uses an analysis of variance approach to evaluate the distances between clusters. The smaller the increase in the total within-group sum of squares as a result of joining two clusters, the "closer" they are. The within-group sum of squares of a cluster is defined as the sum of the squares of the distance between all objects in the cluster and the centroid of the cluster. Ward's method tends to produce compact groups of well-distributed size.