ClusteringMetrics.AverageDistance Property
Definition
Important
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Average Score. For the K-Means algorithm, the 'score' is the distance from the centroid to the example. The average score is, therefore, a measure of proximity of the examples to cluster centroids. In other words, it is a measure of 'cluster tightness'. Note however, that this metric will only decrease if the number of clusters is increased, and in the extreme case (where each distinct example is its own cluster) it will be equal to zero.
public double AverageDistance { get; }member this.AverageDistance : doublePublic ReadOnly Property AverageDistance As DoubleProperty Value
Distance is to the nearest centroid.