after running a clustering algorithm, you are presented with the dendrogram below. what can be concluded about the clustering process given what you see in the dendrogram?

For any organization that needs to identify discrete groups of customers, sales transactions, or other types of behaviors and things, cluster analysis can be a powerful data-mining tool. Insurance companies, for example, use cluster analysis to detect fraudulent claims, and banks use it for credit scoring.

Cluster analysis, like reduced space analysis (factor analysis), is concerned with data matrices that have not been partitioned into criterion versus predictor subsets in advance.

The goal of cluster analysis is to find groups of subjects that are similar in some way, where "similarity" between each pair of subjects refers to some global measure across the entire set of characteristics. In this article, we will look at various clustering methods and the importance of distance as a measure of the proximity of two points.

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