Clustering is defined as an unsupervised classification of a dataset. The objective of the clustering algorithm is to divide the given dataset (a set of points or objects) into groups of data instances or objects (or points) with distance or probabilistic measures. Members in the same groups are closer by distance or similarity or by other measures. In other words, maximize the similarity of the intracluster (internal homogeneity) and minimize the similarity of the intercluster (external separation).
In this chapter, you will learn how to implement the top algorithms for clusters with R; these algorithms are listed here: