Clustering is unsupervised learningUnsupervised learningLearningunsupervised that assigns labels to objects in unlabeled data.Unlabeled dataDataunlabeled When clustering is performed on data that possess class labels, the clusters may or may not correspond with these classes. Cluster partitions may be mathematically represented by sets, partition matrices, and/or cluster prototypes. Sequential clustering (single linkage, complete linkage, average linkage, Ward’s method, etc.) yields hierarchical cluster structures but is computationally expensive. Partitional clustering can be based on hard, fuzzy, possibilistic, or noise clustering models. Cluster prototypes can have different shapes such as hyperspheres, ellipsoids, lines, circles, or more complex shapes. Relational clustering finds clusters in relational data, often enhanced by kernelization. Cluster tendency assessment finds out if the data possess a cluster structure at all, and cluster validity measures help identify the number of clusters or other algorithmic parameters. Clustering can also be done by heuristic methods such as self-organizing maps.