k均值聚类python实现

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.datasets import make_blobs  # 生成数据

X, y = make_blobs()
plt.scatter(X[:, 0], X[:, 1], c=y)
plt.show()
data = X  
print(data)
data = (np.array(data))
(r, c) = np.shape(data)
n_clusters = int(input("n_clusters:"))
cluster = np.array([])
c_max_list = np.max(data, axis=0)
c_min_list = np.min(data, axis=0)
for i in range(c):
    if (i == 0):
        cluster = np.random.uniform(c_min_list[i], c_max_list[i], (n_clusters, 1))
    else:
        cluster = np.c_[cluster, np.random.uniform(c_min_list[i], c_max_list[i], (n_clusters, 1))]
print(cluster)
final_type = []
while 1:
    data_type = np.zeros(r)
    update = np.zeros((n_clusters, c + 1))
    for i in range(r):  # 数据行
        length = 0
        length_j = 0
        for j in range(n_clusters):  # 簇中心
            length1 = 0
            for k in range(c):  # 数据列
                length1 += (cluster[j][k] - data[i][k]) ** 2
            if length == 0:
                length = length1
            else:
                if length1 < length:
                    length = length1
                    length_j = j
        data_type[i] = length_j
        update[length_j][0:c] += data[i][:]
        update[length_j][c] += 1
    judge = 1
    for i in range(n_clusters):
        for j in range(c):
            if update[i][c] != 0:
                update[i][j] = update[i][j] / update[i][c]
                if update[i][j] != cluster[i][j]:
                    judge = 0
                    cluster[i][j] = update[i][j]
            else:
                for jj in range(c):
                    cluster[i][jj] = np.random.uniform(c_min_list[jj], c_max_list[jj])
                judge = 0
                break
    if judge:
        final_type = data_type
        break
print(data_type)
plt.scatter(X[:, 0], X[:, 1], c=data_type)
plt.show()

运行结果：

[[ -6.20944523  -7.33166233]
 [ -6.59508332  -6.7686606 ]
 [ -5.86770245  -6.04789805]
 [ -4.9669678   -7.0455184 ]
 [  7.70549982   8.10100282]
 [  4.00735285  -7.9514294 ]
 [  6.09639615   8.91574546]
 [ -3.73500463  -7.19508706]
 [ -7.23302282  -8.13058997]
 [  7.94516753  10.35617676]
 [ -7.29664332  -6.59538103]
 [ -5.42210382  -7.17325291]
 [  9.91655471   9.95702789]
 [ -5.85890928  -6.95543785]
 [  2.95112483  -8.42581647]
 [  2.87843225  -5.79839309]
 [ 10.00666879   9.47464151]
 [  8.57732658   8.61403764]
 [  3.34740723  -5.96096412]
 [  7.50539769   9.59745877]
 [ -6.80224581  -6.66214873]
 [ 10.06946677  10.99349945]
 [ -6.224985    -7.58182892]
 [  9.20856985   9.30139934]
 [  9.66365481   8.87359162]
 [  9.99306839  10.22089127]
 [ -6.15264246  -8.25664891]
 [ -5.99608561  -6.2647838 ]
 [  6.36115813  -8.80321234]
 [  4.62643508  -6.76776824]
 [ -5.81652698  -6.76163874]
 [  4.14544423  -6.8583864 ]
 [  2.51332026  -7.83183723]
 [  8.25002436  10.51595518]
 [ -7.77566223  -6.29449878]
 [  8.93876589   9.52540323]
 [ -6.52407223  -5.77372853]
 [  9.53944164  10.06880603]
 [  8.35561852  11.88595248]
 [ -7.25885238  -7.23222087]
 [  8.32330758   9.94445983]
 [ -5.68727598  -5.29916468]
 [  9.07573593   8.26142339]
 [  8.07989415   9.41109159]
 [ -6.92155563  -7.49079317]
 [  3.33945894  -6.22464168]
 [ 10.13921133  11.25100618]
 [ -5.48548598  -6.06290165]
 [ -5.31428808  -4.84456997]
 [ -4.20504992  -7.17575386]
 [ -7.2808905   -7.97642   ]
 [  3.02584676  -4.63254456]
 [  8.94365396   9.41844164]
 [  2.82423067  -7.01582269]
 [  3.5629921   -7.23172739]
 [  2.13613233  -7.4896424 ]
 [  7.36758375  10.94971455]
 [  1.77340912  -7.63109169]
 [  3.90272275  -6.98553468]
 [  8.06326528   9.97043944]
 [  7.90103924   9.26937239]
 [  4.59806129  -8.28191975]
 [  8.48335298   9.27544851]
 [  9.27745897   8.20821306]
 [  9.43323104   8.61626946]
 [  3.79131125  -7.89538588]
 [  2.18809797  -8.67136836]
 [  8.22021414   9.84349258]
 [  4.20373867 -10.16628358]
 [  2.49532158  -7.90224922]
 [  4.34213995  -7.71632031]
 [  8.99497767   9.67206088]
 [  2.32636392  -8.50557182]
 [  3.54678388  -7.54218573]
 [  9.11802572  11.54065254]
 [  3.11620005  -7.55287811]
 [  3.55112647  -6.24650294]
 [  2.33221745  -7.44507088]
 [ -6.75276884  -5.47180945]
 [  5.49777598  -8.97042451]
 [  2.88320675  -7.01387697]
 [ -5.65060663  -8.15050371]
 [  9.65124143   8.79529544]
 [ -5.15453426  -5.26414948]
 [ -5.89042596  -7.71697968]
 [  3.43598122  -9.27241805]
 [  6.6773825    9.33694274]
 [  8.9237238    9.80815102]
 [  2.02021482 -10.12175901]
 [ -6.83701235  -6.95518246]
 [ -6.13688258  -7.27710626]
 [ -5.52900517  -6.12941057]
 [  9.84790676  11.67213138]
 [ -5.26072352  -6.67435566]
 [  8.89548533   9.31082552]
 [  3.80867113  -9.29292117]
 [  2.69777319  -8.95656029]
 [  0.98534355  -8.95935225]
 [ -5.13337288  -7.07455859]
 [ -6.83534647  -8.63788616]]
n_clusters:3
[[ 3.02646644  8.96311605]
 [-3.91623975 -0.34736316]
 [ 4.65563666 -6.59711839]]
[1. 1. 1. 1. 0. 2. 0. 1. 1. 0. 1. 1. 0. 1. 2. 2. 0. 0. 2. 0. 1. 0. 1. 0.
 0. 0. 1. 1. 2. 2. 1. 2. 2. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 2. 0. 1.
 1. 1. 1. 2. 0. 2. 2. 2. 0. 2. 2. 0. 0. 2. 0. 0. 0. 2. 2. 0. 2. 2. 2. 0.
 2. 2. 0. 2. 2. 2. 1. 2. 2. 1. 0. 1. 1. 2. 0. 0. 2. 1. 1. 1. 0. 1. 0. 2.
 2. 2. 1. 1.]