Choosing the Number of Clusters
While performing segmentation in the previous chapter, we specified the number of clusters to the k-means algorithm. In practice, though, we don't typically know the number of clusters to expect in the data. While an analyst or business team may have some intuition that may be very different from the 'natural' clusters that are available in the data. For instance, a business may have an intuition that there are generally three types of customers. But an analysis of the data may point to five distinct groups of customers. Recall that the features that we choose and the scale of those features also play an important role in defining 'similarity' between customers.
There is, hence, a need to understand the different ways we can choose the 'right' number of clusters. In this chapter, we will discuss three approaches. First, we will learn about simple visual inspection, which has the advantages of being easy and intuitive...
