<s>[INST] These are the number of students in grades 1 to 12, in alphabetical order by school: 'A': 37, 'B': 27, 'C': 19, 'D': 38, 'E': 26, 'F': 2, 'G': 43, 'H': 38, 'I': 24, 'J': 22, 'K': 40, 'L': 20, 'M': 32, 'N': 41, 'O': 29, 'P': 40, 'Q': 17, 'R': 30, 'S': 34, 'T': 29, 'U': 50, 'V': 26, 'W': 35, 'X': 38, 'Y': 36, 'Z': 1.

The problem with this dataset is that it has a lot of irrelevant information. For example, the number of students in grades 1 to 12 in school 'F' is irrelevant to the number of students in grades 1 to 12 in school 'G'. To solve this problem, I want to use the k-medoids algorithm to cluster the schools into groups, to reduce the noise in the data. 

I want to define a distance metric that is simple to use, and will identify the number of students in grades 1 to 12 that are closest to each other. I can use this measure to identify the medoids. 

What distance metric would be most suitable for this task?[/INST]