Clustering is an unsupervised learning finding patterns in unlabeled data Clustering - Types Type Description Example Algorithms Problem Hard Clustering assigns an instance to 1 cluster clusters do not overlap element either belongs to a cluster or it does not Hierarchical Clustering k-Means Clustering each point/instance is given a “hard” assignment to exactly one cluster-center does not allow uncertainty in cluster-center/class-membership does not allow point/instance to belong to more than one cluster-center Soft Clustering assigns probabilities that an instance belongs to each cluster clusters may overlap strength of association between clusters and instances EM - Clustering Fuzzy C-Means (FCM) Clustering Clustering - Other Community Detection vs Clustering Clustering - Algorithm Comparisons