in these methodsthe clusters are formed by portioning the objects into clusters number of clusters will be equal to the number of partitions ex -meansclustering large applications based upon randomized search (claransgrid in these methodsthe clusters are formed as grid like structure the advantage of these methods is that all the clustering operation done on these grids are fast and independent of the number of data objects ex statistical information grid (sting)clustering in quest (cliquemeasuring clustering performance one of the most important consideration regarding ml model is assessing its performance or you can say model' quality in case of supervised learning algorithmsassessing the quality of our model is easy because we already have labels for every example on the other handin case of unsupervised learning algorithms we are not that much blessed because we deal with unlabeled data but still we have some metrics that give the practitioner an insight about the happening of change in clusters depending on algorithm before we deep dive into such metricswe must understand that these metrics only evaluates the comparative performance of models against each other rather than measuring the validity of the model' prediction followings are some of the metrics that we can deploy on clustering algorithms to measure the quality of modelsilhouette analysis silhouette analysis used to check the quality of clustering model by measuring the distance between the clusters it basically provides us way to assess the parameters like number of clusters with the help of silhouette score this score measures how close each point in one cluster is to points in the neighboring clusters analysis of silhouette score the range of silhouette score is [- its analysis is as follows+ score:near + silhouette score indicates that the sample is far away from its neighboring cluster score: silhouette score indicates that the sample is on or very close to the decision boundary separating two neighboring clusters - score- silhouette score indicates that the samples have been assigned to the wrong clusters the calculation of silhouette score can be done by using the following formulasilhouette score ( )/max(pqherep mean distance to the points in the nearest cluster andq mean intra-cluster distance to all the points