【Python数据挖掘课程】三.Kmeans聚类代码实现、作业及优化

1、Python.Kmeans聚类代码实现、作业及优化. 案例实现01. assists_per_minute height time_played age points_per_minute02.03.04.05.06.07.08.09.10.11.12. 1013. 1114. 1215. 1316. 1417. 1518. 1619. 1720. 1821. 1922. 2001234567890.0888 2010.1399 1980.0747 1980.0983 1910.1276 1960.1671 2010.1906 1930.1061 1910.2446 1850.1670 203

2、0.2485 1880.1227 1980.1240 1850.1461 1910.2315 1910.0494 1930.1107 1960.2521 1830.1007 1930.1067 1960.1956 18836.02 2839.32 3038.80 2640.71 3038.40 2834.10 3136.20 3036.75 2738.43 2933.54 2435.01 2736.67 2933.88 2435.59 3038.01 2832.38 3235.22 2531.73 2928.81 3435.60 2335.28 320.58850.82910.49740.57

3、720.57030.58350.52760.55230.40070.47700.43130.49090.56680.51130.37880.55900.47990.57350.63180.43260.428006. X = 0.0888, 0.5885,07.08.09.10.11.12.13.14.15.16.17.18.19.20.21.22.0.1399, 0.8291,0.0747, 0.4974,0.0983, 0.5772,0.1276, 0.5703,0.1671, 0.5835,0.1906, 0.5276,0.1061, 0.5523,0.2446, 0.4007,0.167

4、0, 0.4770,0.2485, 0.4313,0.1227, 0.4909,0.1240, 0.5668,0.1461, 0.5113,0.2315, 0.3788,0.0494, 0.5590,0.1107, 0.4799,30. # Kmeans 聚类31. clf = KMeans(n_clusters=3)32. y_pred = clf.fit_predict(X)33. print(clf)34. print(y_pred)35.46. plt.scatter(x, y, c=y_pred, maer=47. plt.titeans-Basketball Data)48. pl

5、t.xlabessists_per_minute49. plt.ylabeoints_per_minute)50. plt.legendank)51. plt.show()01. from sklearn.clustport KMeans 01. X = 164,62,156,50,.X 01. clf = KMeans(n_clusters=3) 01. y_pred =clf.fit_predict(X) 3类，类标为0、1、2。01. print(y_pred) 200000010100010000001. import matplotlib.pyplot as plt01. x = n

6、0for nin X02. y = n1for nin X 01. plt.scatter(x, y, c=y_pred, marker=o)01. plt.title(Kmeans-Basketball Data)。01. plt.xlabel(assists_per_minute)01. plt.ylabel(points_per_minute)01. plt.legend(Rank)01. plt.show(). 学图例 / 学。三. Matplotlib绘图优化python01. 0.0888 201 36.02 28 0.588502. 0.1399 198 39.32 30 0.8

7、29103. 0.0747 198 38.8 26 0.497404. 0.0983 191 40.71 30 0.577205. 0.1276 196 38.4 28 0.570306. 0.1671 201 34.1 31 0.583507. 0.1906 193 36.2 30 0.527608. 0.1061 191 36.75 27 0.552309. 0.2446 185 38.43 29 0.400710. 0.167 203 33.54 24 0.47711. 0.2485 188 35.01 27 0.431312. 0.1227 198 36.67 29 0.490913.

8、 0.124 185 33.88 24 0.566814. 0.1461 191 35.59 30 0.511315. 0.2315 191 38.01 28 0.378816. 0.0494 193 32.38 32 0.55917. 0.1107 196 35.22 25 0.479918. 0.2521 183 31.73 29 0.573519. 0.1007 193 28.81 34 0.631820. 0.1067 196 35.6 23 0.432621. 0.1956 188 35.28 32 0.42822. 0.1828 191 29.54 28 0.440123. 0.1

9、627 196 31.35 28 0.558124. 0.1403 198 33.5 23 0.486625. 0.1563 193 34.56 32 0.526726. 0.2681 183 39.53 27 0.543927. 0.1236 196 26.7 34 0.441928. 0.13 188 30.77 26 0.399829. 0.0896 198 25.67 30 0.432530. 0.2071 178 36.22 30 0.408631. 0.2244 185 36.55 23 0.462432. 0.3437 185 34.91 31 0.432533. 0.1058

10、191 28.35 28 0.490334. 0.2326 185 33.53 27 0.480235. 0.1577 193 31.07 25 0.434536. 0.2327 185 36.52 32 0.481937. 0.1256 196 27.87 29 0.624438. 0.107 198 24.31 34 0.399139. 0.1343 193 31.26 28 0.441440. 0.0586 196 22.18 23 0.401341. 0.2383 185 35.25 26 0.380142. 0.1006 198 22.87 30 0.349843. 0.2164 1

11、93 24.49 32 0.318544. 0.1485 198 23.57 27 0.309745. 0.227 191 31.72 27 0.431946. 0.1649 188 27.9 25 0.379947. 0.1188 191 22.74 24 0.409148. 0.194 193 20.62 27 0.358849. 0.2495 185 30.46 25 0.472750. 0.2378 185 32.38 27 0.321251. 0.1592 191 25.75 31 0.341852. 0.2069 170 33.84 30 0.428553. 0.2084 185

12、27.83 25 0.391754. 0.0877 193 21.67 26 0.576955. 0.101 193 21.79 24 0.477356. 0.0942 201 20.17 26 0.451257. 0.055 193 29.07 31 0.309658. 0.1071 196 24.28 24 0.308959. 0.0728 193 19.24 27 0.457360. 0.2771 180 27.07 28 0.321461. 0.0528 196 18.95 22 0.543762. 0.213 188 21.59 30 0.412163. 0.1356 193 13.

13、27 31 0.218564. 0.1043 196 16.3 23 0.331365. 0.113 191 23.01 25 0.330266. 0.1477 196 20.31 31 0.467767. 0.1317 188 17.46 33 0.240668. 0.2187 191 21.95 28 0.300769. 0.2127 188 14.57 37 0.247170. 0.2547 160 34.55 28 0.289471. 0.1591 191 22.0 24 0.368272. 0.0898 196 13.37 34 0.38973. 0.2146 188 20.51 2

14、4 0.51274. 0.1871 183 19.78 28 0.444975. 0.1528 191 16.36 33 0.403576. 0.156 191 16.03 23 0.268377. 0.2348 188 24.27 26 0.271978. 0.1623 180 18.49 28 0.340879. 0.1239 180 17.76 26 0.439380. 0.2178 185 13.31 25 0.300481. 0.1608 185 17.41 26 0.350382. 0.0805 193 13.67 25 0.438883. 0.1776 193 17.46 27

15、0.257884. 0.1668 185 14.38 35 0.298985. 0.1072 188 12.12 31 0.445586. 0.1821 185 12.63 25 0.308787. 0.188 180 12.24 30 0.367888. 0.1167 196 12.0 24 0.366789. 0.2617 185 24.46 27 0.318990. 0.1994 188 20.06 27 0.418791. 0.1706 170 17.0 25 0.505992. 0.1554 183 11.58 24 0.319593. 0.2282 185 10.08 24 0.2

16、38194. 0.1778 185 18.56 23 0.280295. 0.1863 185 11.81 23 0.38196. 0.1014 193 13.81 32 0.1593python08.13.14.15.16.17.18.19.20.21.22.#数据存储datadata.append(s)31.32.33.34.35.1 0.1399 198 39.32 30 0.8291,2 0.0747 198 38.80 26 0.4974,3 0.0983 191 40.71 30 0.5772,4 0.1276 196 38.40 28 0.570355.64. KMeans聚类8

17、1. Python导Matplotlib包，专门于绘图82. import matplotlib.pyplot as plt此处as相当于重命名，plt于显图像83. 84.85. importnumpy as np86. importmatplotlib.pyplot as plt87.88.89. #获取第列和第列数据使循环获取 表X第列90. x = n0for nin X91. printx92. y = n1for nin X93. printy94.95. #绘制散点图参数：x横轴 纵轴 c=y_pred聚类预测结果类型 表圆点 表星型 表点96. #plt.scatter(x,

18、y, c=y_pred, marker=x)97.98.99. #坐标100. x1 = 101. y1 = 112.113.114.115.116.117.118.119.120.121.122.123.124.i = i + 1125. #四种颜红 绿 蓝 126. plot1, = plt.plot(x1, y1,or, marker=x)127. plot2, = plt.plot(x2, y2,og, marker=o)128. plot3, = plt.plot(x3, y3,ob, marker=*)129.130. #绘制标题131. plt.title(Kmeans-Basketball Data)132