davis-bouldin index db index is another good metric to perform the analysis of clustering algorithms with the help of db indexwe can understand the following points about clustering modelweather the clusters are well-spaced from each other or nothow much dense the clusters arewe can calculate db index with the help of following formulan db maxji (ci cj = heren number of clusters average distance of all points in cluster from the cluster centroid ci less the db indexbetter the clustering model is dunn index it works same as db index but there are following points in which both differsthe dunn index considers only the worst case the clusters that are close together while db index considers dispersion and separation of all the clusters in clustering model dunn index increases as the performance increases while db index gets better when clusters are well-spaced and dense we can calculate dunn index with the help of following formuladmin <= < <= (ijmax <= < <= (khereijk each indices for clusters inter-cluster distance intra-cluster distance types of ml clustering algorithms the following are the most important and useful ml clustering algorithmsk-means clustering this clustering algorithm computes the centroids and iterates until we it finds optimal centroid it assumes that the number of clusters are already known it is also called flat clustering algorithm the number of clusters identified from data by algorithm is represented by 'kin -means mean-shift algorithm it is another powerful clustering algorithm used in unsupervised learning unlike -means clusteringit does not make any assumptions hence it is non-parametric algorithm