Clustering is a process that groups a set of objects into several subsets which are called clusters. The goal of the clustering process is to define the clusters in a way that each object and the object that is not more similar to another object are member of the same cluster. In other words, objects within a cluster should be most similar while objects of different clusters should be most different to each other.

There are many purposes for performing clustering such as data reduction or hypothesis generation. Subsection Purpose of Clusteringdescribes the different purposes of clustering.

There are several categories of clustering algorithms including partitioning clustering algorithms, hierarchical clustering algorithms, density-based clustering algorithms, or grid-based clustering algorithms. This dissertation sketches partitioning clustering algorithms and hierarchical clustering algorithms in the succeeding subsections because the clustering algorithm presented in chapter \ref{chad} is a combined algorithm consisting of a partitioning clustering algorithm and a hierarchical clustering algorithm.

This section concludes with a subsection discussing quality measure of clustering. Especially, that subsection focusses on the quality measures used in the experiments of of the approach of this dissertation.