Evaluation

Distancia minima entre puntos de diferentes clusters

Davies–Bouldin 指数（越小越好）。

已重构为委托统一实现 ClusteringIndices.DaviesBouldin()。

把聚类模型转换为统一指标模块所需的「特征矩阵 + 整数簇标签」输入。

Calinski–Harabasz 指数（越大越好）。

已重构为委托统一实现 ClusteringIndices.CalinskiHarabasz()。

最大簇内直径。

已重构为委托统一实现 ClusteringIndices.MaximumDiameter()。

Silhouette Coefficient

Silhouette Coefficient

Silhouette score is used to evaluate the quality of clusters created using clustering algorithms such as K-Means in terms of how well samples are clustered with other samples that are similar to each other. The Silhouette score is calculated for each sample of different clusters. To calculate the Silhouette score for each observation/data point, the following distances need to be found out for each observations belonging to all the clusters:

Mean distance between the observation And all other data points In the same cluster. This distance can also be called a mean intra-cluster distance. The mean distance Is denoted by a Mean distance between the observation And all other data points Of the Next nearest cluster. This distance can also be called a mean nearest-cluster distance. The mean distance Is denoted by b

Silhouette score, S, for Each sample Is calculated Using the following formula:

(S = \frac{(b - a)}{max(a, b)})

The value Of the Silhouette score varies from -1 To 1. If the score Is 1, the cluster Is dense And well-separated than other clusters. A value near 0 represents overlapping clusters With samples very close To the decision boundary Of the neighboring clusters. A negative score [-1, 0] indicates that the samples might have got assigned To the wrong clusters.

Dunn Index

Dunn 指数（越大越好）。

已重构为委托统一实现 ClusteringIndices.Dunn()。