*It was announced at unisba mathematics seminar in 2014*

**Abstract**

Cluster Analysis is a data grouping analysis that groups data based on the information specified in the data. The purpose of cluster analysis is for objects in one group to have similarities to each other whereas with different objects the groups have differences. The process of analyzing clusters or grouping data can be done with two methods: hierarchy method and non-hierarchical method. The hierarchy method is divided into two, the *agglomerative* and *divisive *methods. In this article, the hierarchy method will be discussed in the form of *a divisive method. *

At each step in the divisive method *there is *an addition of groups into the values of the two smallest values until finally all the elements are joined. This *divisive* method is a clustering process based on the average value equation between objects. If an object has the largest average value equation then it will split and turn into a *splinter group.* At the end of this article will discuss the results of the *implementation of divisive* methods on ten data on air pollution levels in the United States.