# Divisive Hierarchical Clustering: Example & Analysis

Instructor: David Gloag
In this lesson, we'll take a look at the concept of divisive hierarchical clustering, what it is, an example of its use, and some analysis of how it works.

## Understanding Through Classification

We seem to categorize almost everything we encounter. We want to know a person's gender, we want to know what types of movies our friends like, and we want to know the season we are currently in. You might even say categorizing brings us joy. But why do we care? What is the advantage of classifying things? It boils down to information. We gain understanding concerning the world and the things around us. The act of classifying something gives us details about the object or item being classified. Or, more to the point, the attributes of the class become the attributes of the item.

## What is Clustering?

The word cluster is used interchangeably with class or category. Clustering is a grouping process where the items form clusters. The difference between the items in a cluster is small, and the difference between the clusters themselves is large.

Consider the following list of colors (light green, gray, green, black, dark green, light gray). If we categorize based on color shade, we could wind up with two clusters, one with a base color of green (light green, green, dark green), and one with a base color of gray (light gray, gray, black). The variance between the elements in each cluster is small (shade), and the variance between the clusters is large (base-color).

## What is Hierarchical Clustering?

Hierarchical clustering imposes an order and takes the idea of clustering a step further than basic clustering. It isn't new, you've likely seen hierarchical ordering before. For example, your personal computer's filing system is organized as a hierarchy. The starting level contains folders and files, and each subsequent folder contains more files and folders. Entering each repeats the process again. This can go on for some time. Hierarchical clustering is much the same, except clusters replace folders we had in the example.

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