演算法步驟：

1. 在樣本中隨機選取k個樣本點充當各個簇的中心點；
2. 計算所有樣本點與各個簇中心之間的距離，然後把樣本點劃入最近的簇中；
3. 根據簇中已有的樣本點，重新計算簇中心；
4. 重複步驟2和3，直到簇中心不再改變或改變很小。

Python實現：

import pylab as pl

def calc_e_squire(a, b):
    return (a[0]-b[0]) ** 2 + (a[1]-b[1]) ** 2

# 20 data points
a = [2,4,3,6,7,8,2,3,5,6,12,10,15,16,11,10,19,17,16,13]
b = [5,6,1,4,2,4,3,1,7,9,16,11,19,12,15,14,11,14,11,19]

# initialize two points as the center of the clusters
k1 = [6, 3]
k2 = [6, 1]

# store clusters in lists
sse_k1 = []
sse_k2 = []
while True:
    sse_k1 = []
    sse_k2 = []
    for i in range(20):
        e_squire1 = calc_e_squire(k1, [a[i], b[i]])
        e_squire2 = calc_e_squire(k2, [a[i], b[i]])
        if (e_squire1 <= e_squire2):
            sse_k1.append(i)
        else:
            sse_k2.append(i)

    # change the centers of the clusters
    k1_x = sum([a[i] for i in sse_k1]) / len(sse_k1)
    k1_y = sum([b[i] for i in sse_k1]) / len(sse_k1)

    k2_x = sum([a[i] for i in sse_k2]) / len(sse_k2)
    k2_y = sum([b[i] for i in sse_k2]) / len(sse_k2)

    if k1 != [k1_x, k1_y] or k2 != [k2_x, k2_y]:
        k1 = [k1_x, k1_y]
        k2 = [k2_x, k2_y]
    else:  # the centers of the clusters don't change any more
        break

kv1_x = [a[i] for i in sse_k1]
kv1_y = [b[i] for i in sse_k1]

kv2_x = [a[i] for i in sse_k2]
kv2_y = [b[i] for i in sse_k2]

pl.plot(kv1_x, kv1_y, 'o')
pl.plot(kv2_x, kv2_y, 'or')

pl.xlim(1, 20)
pl.ylim(1, 20)
pl.show()

執行結果：

參考資料：

python實現k均值演算法示例(k均值聚類演算法)