k-means

from math import dist
data = open('27.txt').readlines()
a = []
for e in data:
    x, y = map(float, e.replace(',', '.').split())
    a.append((x, y))
cen = [(-7.9, -4.9), (-3.5, 7.2), (3.6, 8.0)]
cluster = [[], [], []]
for _ in range(100):
    for e in a:
        if dist(e, cen[0]) < dist(e, cen[1]) and dist(e, cen[0]) < dist(e, cen[2]):
            cluster[0].append(e)
        elif dist(e, cen[1]) < dist(e, cen[0]) and dist(e, cen[1]) < dist(e, cen[2]):
            cluster[1].append(e)
        else:
            cluster[2].append(e)
        #### альтернативный вариант вместо ифов
        #dis = [dist(e, cen[0]), dist(e, cen[1]), dist(e, cen[2])]
        #cluster[dis.index(min(dis))].append(e)
    new_cen = []
    for i in range(3):
        best_c = (-1, -1)
        best_rst = 100000000
        for c in cluster[i]:
            rst = 0
            for e in cluster[i]:
                rst += dist(e, c)
            if rst < best_rst:
                best_rst = rst
                best_c = c
        new_cen.append(best_c)
    if new_cen == cen:
        cen = new_cen
        break
    cen = new_cen
rx = abs(sum(c[0] for c in cen)) / 3
ry = abs(sum(c[1] for c in cen)) / 3
print(int(rx * 10000), int(ry * 10000))