python实现K-means 并进行演示，,首先提供一个一维数组

python实现K-means 并进行演示，,首先提供一个一维数组

首先提供一个一维数组，将其第一列和第五列取出来，并进行kmeans计算，接着在python中进行绘图。

from sklearn.cluster import Birch
from sklearn.cluster import KMeans

"""
第二部分：数据集
X表示二维矩阵数据，篮球运动员比赛数据
总共20行，每行两列数据
第一列表示球员每分钟助攻数：assists_per_minute
第二列表示球员每分钟得分数：points_per_minute
"""

X = [
0.0888, 201, 36.02, 28, 0.5885,
0.1399, 198, 39.32, 30, 0.8291,
0.0747, 198, 38.8, 26, 0.4974,
0.0983, 191, 40.71, 30, 0.5772,
0.1276, 196, 38.4, 28, 0.5703,
0.1671, 201, 34.1, 31, 0.5835,
0.1906, 193, 36.2, 30, 0.5276,
0.1061, 191, 36.75, 27, 0.5523,
0.2446, 185, 38.43, 29, 0.4007,
0.167, 203, 33.54, 24, 0.477,
0.2485, 188, 35.01, 27, 0.4313,
0.1227, 198, 36.67, 29, 0.4909,
0.124, 185, 33.88, 24, 0.5668,
0.1461, 191, 35.59, 30, 0.5113,
0.2315, 191, 38.01, 28, 0.3788,
0.0494, 193, 32.38, 32, 0.559,
0.1107, 196, 35.22, 25, 0.4799,
0.2521, 183, 31.73, 29, 0.5735,
0.1007, 193, 28.81, 34, 0.6318,
0.1067, 196, 35.6, 23, 0.4326,
0.1956, 188, 35.28, 32, 0.428,
0.1828, 191, 29.54, 28, 0.4401,
0.1627, 196, 31.35, 28, 0.5581,
0.1403, 198, 33.5, 23, 0.4866,
0.1563, 193, 34.56, 32, 0.5267,
0.2681, 183, 39.53, 27, 0.5439,
0.1236, 196, 26.7, 34, 0.4419,
0.13, 188, 30.77, 26, 0.3998,
0.0896, 198, 25.67, 30, 0.4325,
0.2071, 178, 36.22, 30, 0.4086,
0.2244, 185, 36.55, 23, 0.4624,
0.3437, 185, 34.91, 31, 0.4325,
0.1058, 191, 28.35, 28, 0.4903,
0.2326, 185, 33.53, 27, 0.4802,
0.1577, 193, 31.07, 25, 0.4345,
0.2327, 185, 36.52, 32, 0.4819,
0.1256, 196, 27.87, 29, 0.6244,
0.107, 198, 24.31, 34, 0.3991,
0.1343, 193, 31.26, 28, 0.4414,
0.0586, 196, 22.18, 23, 0.4013,
0.2383, 185, 35.25, 26, 0.3801,
0.1006, 198, 22.87, 30, 0.3498,
0.2164, 193, 24.49, 32, 0.3185,
0.1485, 198, 23.57, 27, 0.3097,
0.227, 191, 31.72, 27, 0.4319,
0.1649, 188, 27.9, 25, 0.3799,
0.1188, 191, 22.74, 24, 0.4091,
0.194, 193, 20.62, 27, 0.3588,
0.2495, 185, 30.46, 25, 0.4727,
0.2378, 185, 32.38, 27, 0.3212,
0.1592, 191, 25.75, 31, 0.3418,
0.2069, 170, 33.84, 30, 0.4285,
0.2084, 185, 27.83, 25, 0.3917,
0.0877, 193, 21.67, 26, 0.5769,
0.101, 193, 21.79, 24, 0.4773,
0.0942, 201, 20.17, 26, 0.4512,
0.055, 193, 29.07, 31, 0.3096,
0.1071, 196, 24.28, 24, 0.3089,
0.0728, 193, 19.24, 27, 0.4573,
0.2771, 180, 27.07, 28, 0.3214,
0.0528, 196, 18.95, 22, 0.5437,
0.213, 188, 21.59, 30, 0.4121,
0.1356, 193, 13.27, 31, 0.2185,
0.1043, 196, 16.3, 23, 0.3313,
0.113, 191, 23.01, 25, 0.3302,
0.1477, 196, 20.31, 31, 0.4677,
0.1317, 188, 17.46, 33, 0.2406,
0.2187, 191, 21.95, 28, 0.3007,
0.2127, 188, 14.57, 37, 0.2471,
0.2547, 160, 34.55, 28, 0.2894,
0.1591, 191, 22.0, 24, 0.3682,
0.0898, 196, 13.37, 34, 0.389,
0.2146, 188, 20.51, 24, 0.512,
0.1871, 183, 19.78, 28, 0.4449,
0.1528, 191, 16.36, 33, 0.4035,
0.156, 191, 16.03, 23, 0.2683,
0.2348, 188, 24.27, 26, 0.2719,
0.1623, 180, 18.49, 28, 0.3408,
0.1239, 180, 17.76, 26, 0.4393,
0.2178, 185, 13.31, 25, 0.3004,
0.1608, 185, 17.41, 26, 0.3503,
0.0805, 193, 13.67, 25, 0.4388,
0.1776, 193, 17.46, 27, 0.2578,
0.1668, 185, 14.38, 35, 0.2989,
0.1072, 188, 12.12, 31, 0.4455,
0.1821, 185, 12.63, 25, 0.3087,
0.188, 180, 12.24, 30, 0.3678,
0.1167, 196, 12.0, 24, 0.3667,
0.2617, 185, 24.46, 27, 0.3189,
0.1994, 188, 20.06, 27, 0.4187,
0.1706, 170, 17.0, 25, 0.5059,
0.1554, 183, 11.58, 24, 0.3195,
0.2282, 185, 10.08, 24, 0.2381,
0.1778, 185, 18.56, 23, 0.2802,
0.1863, 185, 11.81, 23, 0.381,
0.1014, 193, 13.81, 32, 0.1593]


"""
第三部分：KMeans聚类
clf = KMeans(n_clusters=3) 表示类簇数为3，聚成3类数据
"""
L = []
print(len(X))
for i in range(0,len(X),5):
     M = []
     M.append(X[i])
     M.append(X[i+3])
     L.append(M)
print(L)

clf = KMeans(n_clusters=7)
y_pred = clf.fit_predict(L)

#输出完整Kmeans函数，包括很多省略参数
print(clf)

print(y_pred)


import numpy as np
import matplotlib.pyplot as plt

x = [n[0] for n in L]

y = [n[1] for n in L]

plt.scatter(x,y,c=y_pred,marker=‘x‘)

#绘制标题
plt.title("Kmeans-Basketball Data")

#绘制x轴和y轴坐标
plt.xlabel("assists_per_minute")
plt.ylabel("points_per_minute")

#设置右上角图例
plt.legend(["A","B","C"])

plt.show()


参考文档：
1.https://blog.csdn.net/weixin_38656890/article/details/80447548
2.https://sci2s.ugr.es/keel/dataset.php?cod=1293

python实现K-means 并进行演示