calculates the Davies-Bouldin index.
[DaviesBouldinIndex] = GetDaviesBouldinIndex(Samples, Centers, NormType, CompactnessType)
matrix that includes sample vectors as rows
matrix that includes cluster centers as rows
norm of vectors, must be a scalar greater than zeros, can be %inf
exponent applied to the differences between cluster members and center. This parameter is optional. Default value is one.
This function evaluates the the compactness and separation of clusters by the Davies-Bouldin index. A low Davies-Bouldin index means that the clusters are compact and separated from each other. A high Davies-Bouldin index mean that the clusters overlap strongly. There must be at least two distinct cluster centers. Otherwise the Davies-Bouldin index is not defined.
David L. Davies and Donald W. Bouldin, 'A cluster separation measure', IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 224 - 227, 1979