Gender classification – clustering to classify

The following data is taken from the gender classification example, Problem 6, Chapter 2, Naive Bayes:

Height in cm	Weight in kg	Hair length	Gender
180	75	Short	Male
174	71	Short	Male
184	83	Short	Male
168	63	Short	Male
178	70	Long	Male
170	59	Long	Female
164	53	Short	Female
155	46	Long	Female
162	52	Long	Female
166	55	Long	Female
172	60	Long	?

 

To simplify matters, we will remove the column entitled Hair length. We will also remove the column entitled Gender, since we would like to cluster the people in the table based on their height and weight. We would like to establish whether the eleventh person in the table is more likely to be a man or a woman using clustering:

Height in cm	Weight in kg
180	75
174	71
184	83
168	63
178	70
170	59
164	53
155	46
162	52
166	55
172	60

Analysis

We may apply scaling to the initial data, but to simplify matters, we will use the unscaled data in the algorithm. We will cluster the data we have into two clusters, since there are two possibilities for gender—male or female. Then, we will...