clustering algorithmsmachine overview introduction to clustering clustering methods are one of the most useful unsupervised ml methods these methods are used to find similarity as well as the relationship patterns among data samples and then cluster those samples into groups having similarity based on features clustering is important because it determines the intrinsic grouping among the present unlabeled data they basically make some assumptions about data points to constitute their similarity each assumption will construct different but equally valid clusters for examplebelow is the diagram which shows clustering system grouped together the similar kind of data in different clustersclustering system cluster formation methods it is not necessary that clusters will be formed in spherical form followings are some other cluster formation methodsdensity-based in these methodsthe clusters are formed as the dense region the advantage of these methods is that they have good accuracy as well as good ability to merge two clusters ex density-based spatial clustering of applications with noise (dbscan)ordering points to identify clustering structure (opticsetc hierarchical-based in these methodsthe clusters are formed as tree type structure based on the hierarchy they have two categories namelyagglomerative (bottom up approachand divisive (top down approachex clustering using representatives (cure)balanced iterative reducing clustering using hierarchies (birchetc partitioning