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SilhouetteCoefficient<TInstance> Class

Implements an internal evaluation method that measures how similar an element is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the element is well matched to its own cluster and poorly matched to neighboring clusters. If most elements (average) have a high value, then the clustering configuration is appropriate. If the average is a low or negative value, then the clustering configuration may have too many or too few clusters.
Inheritance Hierarchy
System.Object
  Aglomera.Evaluation.Internal.SilhouetteCoefficient<TInstance>

Namespace:  Aglomera.Evaluation.Internal
Assembly:  Aglomera (in Aglomera.dll) Version: 1.1.0
Syntax
C#
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public class SilhouetteCoefficient<TInstance> : IInternalEvaluationCriterion<TInstance>
where TInstance : Object, IComparable<TInstance>
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Type Parameters

TInstance
The type of instance considered.

The SilhouetteCoefficient<TInstance> type exposes the following members.

Constructors
  NameDescription
Public methodSilhouetteCoefficient<TInstance>
Creates a new SilhouetteCoefficient<TInstance> with given dissimilarity metric.
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Properties
  NameDescription
Public propertyDissimilarityMetric
Gets the metric used by this criterion to measure the dissimilarity / distance between cluster elements.
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Methods
Remarks
Assumptions in [2]: - "Note that the construction [...] depends on the availability of other clusters apart from A, so we have to assume [...] that the number of clusters k is more than one." - "When cluster A contains only a single object it is unclear how u(i) should be defined, and then we simply set s(i) equal to zero. This choice is of course arbitrary, but a value of zero appears to be most neutral." Therefore, Silhouette coefficient punishes outliers and noise, so in the presence of such data we should avoid it. References: [1] - https://en.wikipedia.org/wiki/Silhouette_(clustering) [2] - Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
See Also