comparing and evaluating clustering algorithms one of the challenges in applying clustering algorithms is that it is very hard to assess how well an algorithm workedand to compare outcomes between different algorithms after talking about the algorithms behind -meansagglomerative clusteringand dbscanwe will now compare them on some real-world datasets evaluating clustering with ground truth there are metrics that can be used to assess the outcome of clustering algorithm relative to ground truth clusteringthe most important ones being the adjusted rand index (ariand normalized mutual information (nmi)which both provide quantitative measure between and herewe compare the -meansagglomerative clusteringand dbscan algorithms using ari we also include what it looks like when we randomly assign points to two clusters for comparison (see figure - )clustering