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聚类分析是用于区分易管控、有价值的群组的重要的研究方法。学者用聚类分析来探究群体的相似性和不同。聚类分析的目标是使得相似的事物形成同类组织，因此所属于不同组织的事物就能被明显的区分开来。

原则上来说，鉴于不同组织个体的相似性和不同，要形成同类组织是可行的。具有高度相似性的事物聚集到一起，差异性很大的事物则区分开来。因此，迫切需要一种用于量化事物相似性的方法。

不同的方法开启了不同的途径：最初所有的物体来自一个大的集合体，随后随着不同的小组分离出来，这个大的集合体被一步步分割开。这个过程会一直持续到所有的物体都归属到各自的聚集体当中去。

阶层式聚类分级法是目前最为常见的方法。当然所有物体最后都聚集到一起不具有说服力。相反，群体的持续融合直至寻找到一个中间态，小的聚集体不再存在，这是十分有用的，已经成型的大的同类体通常具有相对更高的相似性。

分级群聚常常用树枝型结构练习图来表现，聚群与个体的关系能够很好展示，聚类融合或者分裂的过程也能够被清楚的观察到。

Cluster analysis is an important method for classification of a many of information in Manageable and meaningful groups. Cluster analysis is used for explorative grouping objects according to their similarity or Non-similarity (distance). Goal of cluster analysis is the formation of homogeneous groups from objects that are possible similar (small distance from each other) whereas the objects from different groups distinguish clearly (large distance from each other).

In principle it is also possible to group persons or objects because of their similarity to each other according to their dissimilarity and distance each other. By the cluster objects with high similarity assigns same clusters and objects low similarity (or with great dissimilarity) assigns different Clusters. Therefore a measure is needed that quantifies the similarity of objects.

Divisive methods start the opposite way: in the beginning all objects from a single, large cluster, which is then divided step by step by as each dissimilar groups are separated, that caused more and more smaller clusters. Let this continue until finally all objects are their own cluster.

Agglomerative hierarchical clustering techniques are by far the most common method. The grouping of all objects in a single large cluster at the end of agglomerative algorithm is of course not conclusive. Useful, rather, in the course of the increasing fusion of the clusters to find an intermediate state until small individual clusters no l onger exist, but in the already-formed larger clusters are relatively homogeneous and can be interpreted content.

A hierarchical clustering is often displayed using a tree-like diagram called a dendogram, which displays both the cluster-subcluster relationship and the order in which the clusters were merged (aglomerative view) or split (divisive view). For sets of thoe dimensional points, such as those that we will use as examples, a hierarchical clustering can aslo be graphically represented using a nested cluster diagram. Figure 1 shows an example of these two types of figures for a set of four two-dimensional points.