The Davies-Bouldin index is an internal clustering evaluation method. The formula is straightforward to implement and requires a distance metric to be given.
From Wikipedia:
Due to the way it is defined, as a function of the ratio of the within cluster scatter, to the between cluster separation, a lower value will mean that the clustering is better. It happens to be the average similarity between each cluster and its most similar one, averaged over all the clusters, where the similarity is defined as Si above. This affirms the idea that no cluster has to be similar to another, and hence the best clustering scheme essentially minimizes the Davies Bouldin Index. This index thus defined is an average over all the i clusters, and hence a good measure of deciding how many clusters actually exists in the data is to plot it against the number of clusters it is calculated over. The number i for which this value is the lowest is a good measure of the number of clusters the data could be ideally classified into. This has applications in deciding the value of k in the kmeans algorithm, where the value of k is not known apriori.
The Davies-Bouldin index is an internal clustering evaluation method. The formula is straightforward to implement and requires a distance metric to be given.
From Wikipedia: