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I am trying to find a way to cluster/group students by their knowledge of different subjects.

Given following as an example:

            Subject 1   Subject 2   Subject 3
Student 1       4           5           1
Student 2       4           5           2
Student 3       5           2           1
Student 4       2           5           5
Student 5       5           5           2
Student 6       4           5           1
Student 7       2           2           5
Student 8       4           4           2
Student 9       1           2           1
Student 10      1           1           3
Student 11      1           2           1
Student 12      3           1           4

Also given that the number of students per group should be between 3-4 (could be anything, I just felt that for this example those boundries make sense)

I would like to group students in such a way, that after grouping, the average value of a subject for a given group would approach the average value for that subject to the best of the ability.

Maybe one way to define this would probably be minimalizing the total variance of each subject between group and total.

How would you suggest approaching this problem? What algorithm would you suggest to use?

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  • $\begingroup$ The average constraint means this problem is not likely to be solved by a standard clustering algorithm. Instead, I would look for approximation algorithms (as the problem "smells" NP-hard). $\endgroup$ – Solomonoff's Secret Sep 24 at 14:13
  • $\begingroup$ Yeah, I did realize that exact best output might be hard to find, and we should rather approximate the output. May be set the Threshold on the variance of means $\endgroup$ – Айбек Жылкайдаров Sep 24 at 16:23

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