House ownership – choosing the number of clusters

Let's take the example from the first chapter regarding house ownership:

Age	Annual income in USD	House ownership status
23	50,000	Non-owner
37	34,000	Non-owner
48	40,000	Owner
52	30,000	Non-owner
28	95,000	Owner
25	78,000	Non-owner
35	13,0000	Owner
32	10,5000	Owner
20	10,0000	Non-owner
40	60,000	Owner
50	80,000	Peter

 

We would like to predict whether Peter is a house owner using clustering.

Analysis

Just as in the first chapter, we will have to scale the data, since the income axis is significantly greater and thus would diminish the impact of the age axis, which actually has a good predictive power in this kind of problem. This...