S_Dbw validity index. If you think the code is useful,please give me a star ^_^!

The S_Dbw implemented here is suitable for evaluating the k-medoids clustering algorithm. The clustering center of the algorithm is different from k-means, which is a specific point. Therefore, some people want to use other clustering algorithms to evaluate, and need to modify the code slightly.

S_Dbw consists of two items, inter-cluster density and intra-cluster variance. When using it to select the hyperparameters of the clustering algorithm, we will choose a set of parameters that minimize the S_Dbw value.

1.Clustering Validity Assessment: Finding the optimal partitioning of a data set

2.Understanding of Internal Clustering Validation Measures

3.csdn blog:S_Dbw