Clustering is a classical, often used, yet quite subtle and complex data operation. Dealing with large datasets often imposes the use of clustering algorithms in order to downsize the data: the goal is to compute a set of classes, also called clusters, to partition the original set . That is, we seek to compute subsets such that and such that (classes do not intersect).
Layout algorithms, for instance, sometimes have poor time complexity behavior and are only accessible to modestly large datasets. Laying out an abstract view of the data is a classical strategy.
The topics of this chapter are divided into two main sections according to the type of data under consideration.