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MinimumEnergyLinkageTInstance Class

Implements the minimum (energy) E-distance method that minimizes a joint between-within measure of distance between clusters.
Inheritance Hierarchy
SystemObject
  Aglomera.LinkageMinimumEnergyLinkageTInstance

Namespace:  Aglomera.Linkage
Assembly:  Aglomera (in Aglomera.dll) Version: 1.1.0
Syntax
C#
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public class MinimumEnergyLinkage<TInstance> : ILinkageCriterion<TInstance>
where TInstance : Object, IComparable<TInstance>
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Type Parameters

TInstance
The type of instance considered.

The MinimumEnergyLinkageTInstance type exposes the following members.

Constructors
  NameDescription
Public methodMinimumEnergyLinkageTInstance
Creates a new MinimumEnergyLinkageTInstance with given dissimilarity metric.
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Properties
  NameDescription
Public propertyDissimilarityMetric
Gets the metric used to measure the dissimilarity / distance between cluster elements according to this linkage criterion.
Public propertyDistanceExponent
Gets or sets the distance exponent in the interval (0, 2].
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Methods
  NameDescription
Public methodCalculate
Calculates the distance / dissimilarity between the two given clusters according to this linkage criterion.
Public methodEquals (Inherited from Object.)
Protected methodFinalize (Inherited from Object.)
Public methodGetHashCode (Inherited from Object.)
Public methodGetType (Inherited from Object.)
Protected methodMemberwiseClone (Inherited from Object.)
Public methodToString (Inherited from Object.)
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Remarks
"This method extends WardsMinimumVarianceLinkageTInstance, by defining a cluster distance and objective function in terms of Euclidean distance, or any power of Euclidean distance in the interval (0, 2]. Ward's method is obtained as the special case when the power is 2. The ability of the proposed extension to identify clusters with nearly equal centers is an important advantage over geometric or cluster center methods" [1]. References: [1] - Szekely, G. J., & Rizzo, M. L. (2005). Hierarchical clustering via joint between-within distances: Extending Ward's minimum variance method. Journal of classification, 22(2), 151-183
See Also