Python聚类分析

发表于 | 更新于 | 分类于 | 阅读次数：
  • Kmean聚类

以下使用的是sklearn自带的鸢尾花数据集

1
2
3
import numpy as np  
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
1
2
3
##加载数据集
from sklearn.datasets import load_iris
iris=load_iris()
1
2
import pandas as pd
import numpy as np
1
np.array(iris)
array({'target_names': array(['setosa', 'versicolor', 'virginica'], 
      dtype='|S10'), 'data': array([[ 5.1,  3.5,  1.4,  0.2],
       [ 4.9,  3. ,  1.4,  0.2],
       [ 4.7,  3.2,  1.3,  0.2],
       [ 4.6,  3.1,  1.5,  0.2],
       [ 5. ,  3.6,  1.4,  0.2],
       [ 5.4,  3.9,  1.7,  0.4],
       [ 4.6,  3.4,  1.4,  0.3],
       [ 5. ,  3.4,  1.5,  0.2],
       [ 4.4,  2.9,  1.4,  0.2],
       [ 4.9,  3.1,  1.5,  0.1],
       [ 5.4,  3.7,  1.5,  0.2],
       [ 4.8,  3.4,  1.6,  0.2],
       [ 4.8,  3. ,  1.4,  0.1],
       [ 4.3,  3. ,  1.1,  0.1],
       [ 5.8,  4. ,  1.2,  0.2],
       [ 5.7,  4.4,  1.5,  0.4],
       [ 5.4,  3.9,  1.3,  0.4],
       [ 5.1,  3.5,  1.4,  0.3],
       [ 5.7,  3.8,  1.7,  0.3],
       [ 5.1,  3.8,  1.5,  0.3],
       [ 5.4,  3.4,  1.7,  0.2],
       [ 5.1,  3.7,  1.5,  0.4],
       [ 4.6,  3.6,  1. ,  0.2],
       [ 5.1,  3.3,  1.7,  0.5],
       [ 4.8,  3.4,  1.9,  0.2],
       [ 5. ,  3. ,  1.6,  0.2],
       [ 5. ,  3.4,  1.6,  0.4],
       [ 5.2,  3.5,  1.5,  0.2],
       [ 5.2,  3.4,  1.4,  0.2],
       [ 4.7,  3.2,  1.6,  0.2],
       [ 4.8,  3.1,  1.6,  0.2],
       [ 5.4,  3.4,  1.5,  0.4],
       [ 5.2,  4.1,  1.5,  0.1],
       [ 5.5,  4.2,  1.4,  0.2],
       [ 4.9,  3.1,  1.5,  0.1],
       [ 5. ,  3.2,  1.2,  0.2],
       [ 5.5,  3.5,  1.3,  0.2],
       [ 4.9,  3.1,  1.5,  0.1],
       [ 4.4,  3. ,  1.3,  0.2],
       [ 5.1,  3.4,  1.5,  0.2],
       [ 5. ,  3.5,  1.3,  0.3],
       [ 4.5,  2.3,  1.3,  0.3],
       [ 4.4,  3.2,  1.3,  0.2],
       [ 5. ,  3.5,  1.6,  0.6],
       [ 5.1,  3.8,  1.9,  0.4],
       [ 4.8,  3. ,  1.4,  0.3],
       [ 5.1,  3.8,  1.6,  0.2],
       [ 4.6,  3.2,  1.4,  0.2],
       [ 5.3,  3.7,  1.5,  0.2],
       [ 5. ,  3.3,  1.4,  0.2],
       [ 7. ,  3.2,  4.7,  1.4],
       [ 6.4,  3.2,  4.5,  1.5],
       [ 6.9,  3.1,  4.9,  1.5],
       [ 5.5,  2.3,  4. ,  1.3],
       [ 6.5,  2.8,  4.6,  1.5],
       [ 5.7,  2.8,  4.5,  1.3],
       [ 6.3,  3.3,  4.7,  1.6],
       [ 4.9,  2.4,  3.3,  1. ],
       [ 6.6,  2.9,  4.6,  1.3],
       [ 5.2,  2.7,  3.9,  1.4],
       [ 5. ,  2. ,  3.5,  1. ],
       [ 5.9,  3. ,  4.2,  1.5],
       [ 6. ,  2.2,  4. ,  1. ],
       [ 6.1,  2.9,  4.7,  1.4],
       [ 5.6,  2.9,  3.6,  1.3],
       [ 6.7,  3.1,  4.4,  1.4],
       [ 5.6,  3. ,  4.5,  1.5],
       [ 5.8,  2.7,  4.1,  1. ],
       [ 6.2,  2.2,  4.5,  1.5],
       [ 5.6,  2.5,  3.9,  1.1],
       [ 5.9,  3.2,  4.8,  1.8],
       [ 6.1,  2.8,  4. ,  1.3],
       [ 6.3,  2.5,  4.9,  1.5],
       [ 6.1,  2.8,  4.7,  1.2],
       [ 6.4,  2.9,  4.3,  1.3],
       [ 6.6,  3. ,  4.4,  1.4],
       [ 6.8,  2.8,  4.8,  1.4],
       [ 6.7,  3. ,  5. ,  1.7],
       [ 6. ,  2.9,  4.5,  1.5],
       [ 5.7,  2.6,  3.5,  1. ],
       [ 5.5,  2.4,  3.8,  1.1],
       [ 5.5,  2.4,  3.7,  1. ],
       [ 5.8,  2.7,  3.9,  1.2],
       [ 6. ,  2.7,  5.1,  1.6],
       [ 5.4,  3. ,  4.5,  1.5],
       [ 6. ,  3.4,  4.5,  1.6],
       [ 6.7,  3.1,  4.7,  1.5],
       [ 6.3,  2.3,  4.4,  1.3],
       [ 5.6,  3. ,  4.1,  1.3],
       [ 5.5,  2.5,  4. ,  1.3],
       [ 5.5,  2.6,  4.4,  1.2],
       [ 6.1,  3. ,  4.6,  1.4],
       [ 5.8,  2.6,  4. ,  1.2],
       [ 5. ,  2.3,  3.3,  1. ],
       [ 5.6,  2.7,  4.2,  1.3],
       [ 5.7,  3. ,  4.2,  1.2],
       [ 5.7,  2.9,  4.2,  1.3],
       [ 6.2,  2.9,  4.3,  1.3],
       [ 5.1,  2.5,  3. ,  1.1],
       [ 5.7,  2.8,  4.1,  1.3],
       [ 6.3,  3.3,  6. ,  2.5],
       [ 5.8,  2.7,  5.1,  1.9],
       [ 7.1,  3. ,  5.9,  2.1],
       [ 6.3,  2.9,  5.6,  1.8],
       [ 6.5,  3. ,  5.8,  2.2],
       [ 7.6,  3. ,  6.6,  2.1],
       [ 4.9,  2.5,  4.5,  1.7],
       [ 7.3,  2.9,  6.3,  1.8],
       [ 6.7,  2.5,  5.8,  1.8],
       [ 7.2,  3.6,  6.1,  2.5],
       [ 6.5,  3.2,  5.1,  2. ],
       [ 6.4,  2.7,  5.3,  1.9],
       [ 6.8,  3. ,  5.5,  2.1],
       [ 5.7,  2.5,  5. ,  2. ],
       [ 5.8,  2.8,  5.1,  2.4],
       [ 6.4,  3.2,  5.3,  2.3],
       [ 6.5,  3. ,  5.5,  1.8],
       [ 7.7,  3.8,  6.7,  2.2],
       [ 7.7,  2.6,  6.9,  2.3],
       [ 6. ,  2.2,  5. ,  1.5],
       [ 6.9,  3.2,  5.7,  2.3],
       [ 5.6,  2.8,  4.9,  2. ],
       [ 7.7,  2.8,  6.7,  2. ],
       [ 6.3,  2.7,  4.9,  1.8],
       [ 6.7,  3.3,  5.7,  2.1],
       [ 7.2,  3.2,  6. ,  1.8],
       [ 6.2,  2.8,  4.8,  1.8],
       [ 6.1,  3. ,  4.9,  1.8],
       [ 6.4,  2.8,  5.6,  2.1],
       [ 7.2,  3. ,  5.8,  1.6],
       [ 7.4,  2.8,  6.1,  1.9],
       [ 7.9,  3.8,  6.4,  2. ],
       [ 6.4,  2.8,  5.6,  2.2],
       [ 6.3,  2.8,  5.1,  1.5],
       [ 6.1,  2.6,  5.6,  1.4],
       [ 7.7,  3. ,  6.1,  2.3],
       [ 6.3,  3.4,  5.6,  2.4],
       [ 6.4,  3.1,  5.5,  1.8],
       [ 6. ,  3. ,  4.8,  1.8],
       [ 6.9,  3.1,  5.4,  2.1],
       [ 6.7,  3.1,  5.6,  2.4],
       [ 6.9,  3.1,  5.1,  2.3],
       [ 5.8,  2.7,  5.1,  1.9],
       [ 6.8,  3.2,  5.9,  2.3],
       [ 6.7,  3.3,  5.7,  2.5],
       [ 6.7,  3. ,  5.2,  2.3],
       [ 6.3,  2.5,  5. ,  1.9],
       [ 6.5,  3. ,  5.2,  2. ],
       [ 6.2,  3.4,  5.4,  2.3],
       [ 5.9,  3. ,  5.1,  1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'DESCR': 'Iris Plants Database\n====================\n\nNotes\n-----\nData Set Characteristics:\n    :Number of Instances: 150 (50 in each of three classes)\n    :Number of Attributes: 4 numeric, predictive attributes and the class\n    :Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm\n        - class:\n                - Iris-Setosa\n                - Iris-Versicolour\n                - Iris-Virginica\n    :Summary Statistics:\n\n    ============== ==== ==== ======= ===== ====================\n                    Min  Max   Mean    SD   Class Correlation\n    ============== ==== ==== ======= ===== ====================\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n    ============== ==== ==== ======= ===== ====================\n\n    :Missing Attribute Values: None\n    :Class Distribution: 33.3% for each of 3 classes.\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n    :Date: July, 1988\n\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\nReferences\n----------\n   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n     Mathematical Statistics" (John Wiley, NY, 1950).\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n     Structure and Classification Rule for Recognition in Partially Exposed\n     Environments".  IEEE Transactions on Pattern Analysis and Machine\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n     on Information Theory, May 1972, 431-433.\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n     conceptual clustering system finds 3 classes in the data.\n   - Many, many more ...\n', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']}, dtype=object)

1
X = iris.data[:, 2:4] ##表示我们只取特征空间中的后两个维度
1
X
array([[ 1.4,  0.2],
       [ 1.4,  0.2],
       [ 1.3,  0.2],
       [ 1.5,  0.2],
       [ 1.4,  0.2],
       [ 1.7,  0.4],
       [ 1.4,  0.3],
       [ 1.5,  0.2],
       [ 1.4,  0.2],
       [ 1.5,  0.1],
       [ 1.5,  0.2],
       [ 1.6,  0.2],
       [ 1.4,  0.1],
       [ 1.1,  0.1],
       [ 1.2,  0.2],
       [ 1.5,  0.4],
       [ 1.3,  0.4],
       [ 1.4,  0.3],
       [ 1.7,  0.3],
       [ 1.5,  0.3],
       [ 1.7,  0.2],
       [ 1.5,  0.4],
       [ 1. ,  0.2],
       [ 1.7,  0.5],
       [ 1.9,  0.2],
       [ 1.6,  0.2],
       [ 1.6,  0.4],
       [ 1.5,  0.2],
       [ 1.4,  0.2],
       [ 1.6,  0.2],
       [ 1.6,  0.2],
       [ 1.5,  0.4],
       [ 1.5,  0.1],
       [ 1.4,  0.2],
       [ 1.5,  0.1],
       [ 1.2,  0.2],
       [ 1.3,  0.2],
       [ 1.5,  0.1],
       [ 1.3,  0.2],
       [ 1.5,  0.2],
       [ 1.3,  0.3],
       [ 1.3,  0.3],
       [ 1.3,  0.2],
       [ 1.6,  0.6],
       [ 1.9,  0.4],
       [ 1.4,  0.3],
       [ 1.6,  0.2],
       [ 1.4,  0.2],
       [ 1.5,  0.2],
       [ 1.4,  0.2],
       [ 4.7,  1.4],
       [ 4.5,  1.5],
       [ 4.9,  1.5],
       [ 4. ,  1.3],
       [ 4.6,  1.5],
       [ 4.5,  1.3],
       [ 4.7,  1.6],
       [ 3.3,  1. ],
       [ 4.6,  1.3],
       [ 3.9,  1.4],
       [ 3.5,  1. ],
       [ 4.2,  1.5],
       [ 4. ,  1. ],
       [ 4.7,  1.4],
       [ 3.6,  1.3],
       [ 4.4,  1.4],
       [ 4.5,  1.5],
       [ 4.1,  1. ],
       [ 4.5,  1.5],
       [ 3.9,  1.1],
       [ 4.8,  1.8],
       [ 4. ,  1.3],
       [ 4.9,  1.5],
       [ 4.7,  1.2],
       [ 4.3,  1.3],
       [ 4.4,  1.4],
       [ 4.8,  1.4],
       [ 5. ,  1.7],
       [ 4.5,  1.5],
       [ 3.5,  1. ],
       [ 3.8,  1.1],
       [ 3.7,  1. ],
       [ 3.9,  1.2],
       [ 5.1,  1.6],
       [ 4.5,  1.5],
       [ 4.5,  1.6],
       [ 4.7,  1.5],
       [ 4.4,  1.3],
       [ 4.1,  1.3],
       [ 4. ,  1.3],
       [ 4.4,  1.2],
       [ 4.6,  1.4],
       [ 4. ,  1.2],
       [ 3.3,  1. ],
       [ 4.2,  1.3],
       [ 4.2,  1.2],
       [ 4.2,  1.3],
       [ 4.3,  1.3],
       [ 3. ,  1.1],
       [ 4.1,  1.3],
       [ 6. ,  2.5],
       [ 5.1,  1.9],
       [ 5.9,  2.1],
       [ 5.6,  1.8],
       [ 5.8,  2.2],
       [ 6.6,  2.1],
       [ 4.5,  1.7],
       [ 6.3,  1.8],
       [ 5.8,  1.8],
       [ 6.1,  2.5],
       [ 5.1,  2. ],
       [ 5.3,  1.9],
       [ 5.5,  2.1],
       [ 5. ,  2. ],
       [ 5.1,  2.4],
       [ 5.3,  2.3],
       [ 5.5,  1.8],
       [ 6.7,  2.2],
       [ 6.9,  2.3],
       [ 5. ,  1.5],
       [ 5.7,  2.3],
       [ 4.9,  2. ],
       [ 6.7,  2. ],
       [ 4.9,  1.8],
       [ 5.7,  2.1],
       [ 6. ,  1.8],
       [ 4.8,  1.8],
       [ 4.9,  1.8],
       [ 5.6,  2.1],
       [ 5.8,  1.6],
       [ 6.1,  1.9],
       [ 6.4,  2. ],
       [ 5.6,  2.2],
       [ 5.1,  1.5],
       [ 5.6,  1.4],
       [ 6.1,  2.3],
       [ 5.6,  2.4],
       [ 5.5,  1.8],
       [ 4.8,  1.8],
       [ 5.4,  2.1],
       [ 5.6,  2.4],
       [ 5.1,  2.3],
       [ 5.1,  1.9],
       [ 5.9,  2.3],
       [ 5.7,  2.5],
       [ 5.2,  2.3],
       [ 5. ,  1.9],
       [ 5.2,  2. ],
       [ 5.4,  2.3],
       [ 5.1,  1.8]])

1
2
3
4
5
6
#绘制数据分布图
plt.scatter(X[:, 0], X[:, 1], c = "green", marker='*', label='point')  
plt.xlabel('petal length')  
plt.ylabel('petal width')  
plt.legend(loc=2)  
plt.show()

png

1
2
3
estimator = KMeans(n_clusters=3)#构造聚类器
estimator.fit(X)#聚类
label_pred = estimator.labels_ #获取聚类标签
1
label_pred
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)

1
2
3
4
5
6
7
8
9
10
11
#绘制k-means结果
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
plt.scatter(x0[:, 0], x0[:, 1], c = "red", marker='o', label='label0')  
plt.scatter(x1[:, 0], x1[:, 1], c = "green", marker='*', label='label1')  
plt.scatter(x2[:, 0], x2[:, 1], c = "blue", marker='+', label='label2')  
plt.xlabel('petal length')  
plt.ylabel('petal width')  
plt.legend(loc=2)  
plt.show()

png

1
X.shape
(150, 2)

1
2
X=X.tolist()
label_pred=label_pred.tolist()
1
X
[[1.4, 0.2],
 [1.4, 0.2],
 [1.3, 0.2],
 [1.5, 0.2],
 [1.4, 0.2],
 [1.7, 0.4],
 [1.4, 0.3],
 [1.5, 0.2],
 [1.4, 0.2],
 [1.5, 0.1],
 [1.5, 0.2],
 [1.6, 0.2],
 [1.4, 0.1],
 [1.1, 0.1],
 [1.2, 0.2],
 [1.5, 0.4],
 [1.3, 0.4],
 [1.4, 0.3],
 [1.7, 0.3],
 [1.5, 0.3],
 [1.7, 0.2],
 [1.5, 0.4],
 [1.0, 0.2],
 [1.7, 0.5],
 [1.9, 0.2],
 [1.6, 0.2],
 [1.6, 0.4],
 [1.5, 0.2],
 [1.4, 0.2],
 [1.6, 0.2],
 [1.6, 0.2],
 [1.5, 0.4],
 [1.5, 0.1],
 [1.4, 0.2],
 [1.5, 0.1],
 [1.2, 0.2],
 [1.3, 0.2],
 [1.5, 0.1],
 [1.3, 0.2],
 [1.5, 0.2],
 [1.3, 0.3],
 [1.3, 0.3],
 [1.3, 0.2],
 [1.6, 0.6],
 [1.9, 0.4],
 [1.4, 0.3],
 [1.6, 0.2],
 [1.4, 0.2],
 [1.5, 0.2],
 [1.4, 0.2],
 [4.7, 1.4],
 [4.5, 1.5],
 [4.9, 1.5],
 [4.0, 1.3],
 [4.6, 1.5],
 [4.5, 1.3],
 [4.7, 1.6],
 [3.3, 1.0],
 [4.6, 1.3],
 [3.9, 1.4],
 [3.5, 1.0],
 [4.2, 1.5],
 [4.0, 1.0],
 [4.7, 1.4],
 [3.6, 1.3],
 [4.4, 1.4],
 [4.5, 1.5],
 [4.1, 1.0],
 [4.5, 1.5],
 [3.9, 1.1],
 [4.8, 1.8],
 [4.0, 1.3],
 [4.9, 1.5],
 [4.7, 1.2],
 [4.3, 1.3],
 [4.4, 1.4],
 [4.8, 1.4],
 [5.0, 1.7],
 [4.5, 1.5],
 [3.5, 1.0],
 [3.8, 1.1],
 [3.7, 1.0],
 [3.9, 1.2],
 [5.1, 1.6],
 [4.5, 1.5],
 [4.5, 1.6],
 [4.7, 1.5],
 [4.4, 1.3],
 [4.1, 1.3],
 [4.0, 1.3],
 [4.4, 1.2],
 [4.6, 1.4],
 [4.0, 1.2],
 [3.3, 1.0],
 [4.2, 1.3],
 [4.2, 1.2],
 [4.2, 1.3],
 [4.3, 1.3],
 [3.0, 1.1],
 [4.1, 1.3],
 [6.0, 2.5],
 [5.1, 1.9],
 [5.9, 2.1],
 [5.6, 1.8],
 [5.8, 2.2],
 [6.6, 2.1],
 [4.5, 1.7],
 [6.3, 1.8],
 [5.8, 1.8],
 [6.1, 2.5],
 [5.1, 2.0],
 [5.3, 1.9],
 [5.5, 2.1],
 [5.0, 2.0],
 [5.1, 2.4],
 [5.3, 2.3],
 [5.5, 1.8],
 [6.7, 2.2],
 [6.9, 2.3],
 [5.0, 1.5],
 [5.7, 2.3],
 [4.9, 2.0],
 [6.7, 2.0],
 [4.9, 1.8],
 [5.7, 2.1],
 [6.0, 1.8],
 [4.8, 1.8],
 [4.9, 1.8],
 [5.6, 2.1],
 [5.8, 1.6],
 [6.1, 1.9],
 [6.4, 2.0],
 [5.6, 2.2],
 [5.1, 1.5],
 [5.6, 1.4],
 [6.1, 2.3],
 [5.6, 2.4],
 [5.5, 1.8],
 [4.8, 1.8],
 [5.4, 2.1],
 [5.6, 2.4],
 [5.1, 2.3],
 [5.1, 1.9],
 [5.9, 2.3],
 [5.7, 2.5],
 [5.2, 2.3],
 [5.0, 1.9],
 [5.2, 2.0],
 [5.4, 2.3],
 [5.1, 1.8]]

1
2
3
4
cluster_result=[]
for i in zip(X,label_pred):
    i[0].append(i[1])
    cluster_result.append(i[0])
1
cluster_result
[[1.4, 0.2, 0],
 [1.4, 0.2, 0],
 [1.3, 0.2, 0],
 [1.5, 0.2, 0],
 [1.4, 0.2, 0],
 [1.7, 0.4, 0],
 [1.4, 0.3, 0],
 [1.5, 0.2, 0],
 [1.4, 0.2, 0],
 [1.5, 0.1, 0],
 [1.5, 0.2, 0],
 [1.6, 0.2, 0],
 [1.4, 0.1, 0],
 [1.1, 0.1, 0],
 [1.2, 0.2, 0],
 [1.5, 0.4, 0],
 [1.3, 0.4, 0],
 [1.4, 0.3, 0],
 [1.7, 0.3, 0],
 [1.5, 0.3, 0],
 [1.7, 0.2, 0],
 [1.5, 0.4, 0],
 [1.0, 0.2, 0],
 [1.7, 0.5, 0],
 [1.9, 0.2, 0],
 [1.6, 0.2, 0],
 [1.6, 0.4, 0],
 [1.5, 0.2, 0],
 [1.4, 0.2, 0],
 [1.6, 0.2, 0],
 [1.6, 0.2, 0],
 [1.5, 0.4, 0],
 [1.5, 0.1, 0],
 [1.4, 0.2, 0],
 [1.5, 0.1, 0],
 [1.2, 0.2, 0],
 [1.3, 0.2, 0],
 [1.5, 0.1, 0],
 [1.3, 0.2, 0],
 [1.5, 0.2, 0],
 [1.3, 0.3, 0],
 [1.3, 0.3, 0],
 [1.3, 0.2, 0],
 [1.6, 0.6, 0],
 [1.9, 0.4, 0],
 [1.4, 0.3, 0],
 [1.6, 0.2, 0],
 [1.4, 0.2, 0],
 [1.5, 0.2, 0],
 [1.4, 0.2, 0],
 [4.7, 1.4, 2],
 [4.5, 1.5, 2],
 [4.9, 1.5, 2],
 [4.0, 1.3, 2],
 [4.6, 1.5, 2],
 [4.5, 1.3, 2],
 [4.7, 1.6, 2],
 [3.3, 1.0, 2],
 [4.6, 1.3, 2],
 [3.9, 1.4, 2],
 [3.5, 1.0, 2],
 [4.2, 1.5, 2],
 [4.0, 1.0, 2],
 [4.7, 1.4, 2],
 [3.6, 1.3, 2],
 [4.4, 1.4, 2],
 [4.5, 1.5, 2],
 [4.1, 1.0, 2],
 [4.5, 1.5, 2],
 [3.9, 1.1, 2],
 [4.8, 1.8, 2],
 [4.0, 1.3, 2],
 [4.9, 1.5, 2],
 [4.7, 1.2, 2],
 [4.3, 1.3, 2],
 [4.4, 1.4, 2],
 [4.8, 1.4, 2],
 [5.0, 1.7, 1],
 [4.5, 1.5, 2],
 [3.5, 1.0, 2],
 [3.8, 1.1, 2],
 [3.7, 1.0, 2],
 [3.9, 1.2, 2],
 [5.1, 1.6, 1],
 [4.5, 1.5, 2],
 [4.5, 1.6, 2],
 [4.7, 1.5, 2],
 [4.4, 1.3, 2],
 [4.1, 1.3, 2],
 [4.0, 1.3, 2],
 [4.4, 1.2, 2],
 [4.6, 1.4, 2],
 [4.0, 1.2, 2],
 [3.3, 1.0, 2],
 [4.2, 1.3, 2],
 [4.2, 1.2, 2],
 [4.2, 1.3, 2],
 [4.3, 1.3, 2],
 [3.0, 1.1, 2],
 [4.1, 1.3, 2],
 [6.0, 2.5, 1],
 [5.1, 1.9, 1],
 [5.9, 2.1, 1],
 [5.6, 1.8, 1],
 [5.8, 2.2, 1],
 [6.6, 2.1, 1],
 [4.5, 1.7, 2],
 [6.3, 1.8, 1],
 [5.8, 1.8, 1],
 [6.1, 2.5, 1],
 [5.1, 2.0, 1],
 [5.3, 1.9, 1],
 [5.5, 2.1, 1],
 [5.0, 2.0, 1],
 [5.1, 2.4, 1],
 [5.3, 2.3, 1],
 [5.5, 1.8, 1],
 [6.7, 2.2, 1],
 [6.9, 2.3, 1],
 [5.0, 1.5, 2],
 [5.7, 2.3, 1],
 [4.9, 2.0, 1],
 [6.7, 2.0, 1],
 [4.9, 1.8, 1],
 [5.7, 2.1, 1],
 [6.0, 1.8, 1],
 [4.8, 1.8, 2],
 [4.9, 1.8, 1],
 [5.6, 2.1, 1],
 [5.8, 1.6, 1],
 [6.1, 1.9, 1],
 [6.4, 2.0, 1],
 [5.6, 2.2, 1],
 [5.1, 1.5, 1],
 [5.6, 1.4, 1],
 [6.1, 2.3, 1],
 [5.6, 2.4, 1],
 [5.5, 1.8, 1],
 [4.8, 1.8, 2],
 [5.4, 2.1, 1],
 [5.6, 2.4, 1],
 [5.1, 2.3, 1],
 [5.1, 1.9, 1],
 [5.9, 2.3, 1],
 [5.7, 2.5, 1],
 [5.2, 2.3, 1],
 [5.0, 1.9, 1],
 [5.2, 2.0, 1],
 [5.4, 2.3, 1],
 [5.1, 1.8, 1]]

接下来将３类数据点分别导出到csv文件

1
2
3
4
5
6
7
8
9
10
11
#分类整合
label0=[]#第０类
label1=[]#第１类
label2=[]##第2类
for i in cluster_result:
    if i[2]==0:
        label0.append(i)
    elif i[2]==1:
        label1.append(i)
    elif i[2]==2:
        label2.append(i)

现在得到的是３个list，我们将先把list转换成array，再进行导出

1
2
3
4
#转化成array
label0=np.array(label0)
label1=np.array(label1)
label2=np.array(label2)
1
2
#预先创建一个空的数据框
label0_csv=pd.DataFrame()
1
2
3
4
#将第０类样本信息进行填充到之前的看破那个数据框
label0_csv['feature1']=label0[:,0]
label0_csv['feature2']=label0[:,1]
label0_csv['kind']=label0[:,2]
1
2
#整合之后的样子
label0_csv
























































































































































































































































































































feature1 feature2 kind
0 1.4 0.2 0.0
1 1.4 0.2 0.0
2 1.3 0.2 0.0
3 1.5 0.2 0.0
4 1.4 0.2 0.0
5 1.7 0.4 0.0
6 1.4 0.3 0.0
7 1.5 0.2 0.0
8 1.4 0.2 0.0
9 1.5 0.1 0.0
10 1.5 0.2 0.0
11 1.6 0.2 0.0
12 1.4 0.1 0.0
13 1.1 0.1 0.0
14 1.2 0.2 0.0
15 1.5 0.4 0.0
16 1.3 0.4 0.0
17 1.4 0.3 0.0
18 1.7 0.3 0.0
19 1.5 0.3 0.0
20 1.7 0.2 0.0
21 1.5 0.4 0.0
22 1.0 0.2 0.0
23 1.7 0.5 0.0
24 1.9 0.2 0.0
25 1.6 0.2 0.0
26 1.6 0.4 0.0
27 1.5 0.2 0.0
28 1.4 0.2 0.0
29 1.6 0.2 0.0
30 1.6 0.2 0.0
31 1.5 0.4 0.0
32 1.5 0.1 0.0
33 1.4 0.2 0.0
34 1.5 0.1 0.0
35 1.2 0.2 0.0
36 1.3 0.2 0.0
37 1.5 0.1 0.0
38 1.3 0.2 0.0
39 1.5 0.2 0.0
40 1.3 0.3 0.0
41 1.3 0.3 0.0
42 1.3 0.2 0.0
43 1.6 0.6 0.0
44 1.9 0.4 0.0
45 1.4 0.3 0.0
46 1.6 0.2 0.0
47 1.4 0.2 0.0
48 1.5 0.2 0.0
49 1.4 0.2 0.0 

1
2
#保存第０类样本信息文件
label0_csv.to_csv(r'/home/fantasy/Desktop/数学建模Python/聚类/鸢尾花聚类结果csv/label0.csv',index=False)

至于其他两类的数据导出方法也一样，这里不再赘述。
其实，可以把这个导出功能封装成一个函数，传入保存路径和第几类就可以了，当然也可以直接来个for循环解决.

到现在，我们都做了些什么呢？来总结一下：

首先，我们导入了sklearn自带的鸢尾花数据集并选取了其中两个特征(feature)，拟用这两个特征做聚类.

接着，我们调用了sklearn的聚类方法做了聚类（聚成了３类），并将样本的特征与所属类别（int）整合在一个list里面，并由外围的list包裹住，然后再将这所有的list按照所属聚类数的不同而归类存储.

最后，将归类的数据先转化成数组形式，然后做成csv文件，导出到指定目录下.

有一点值得注意的是，在可视化的时候只能用二维数据，即两个特征，受维度限制.

接下来我们再来看一个例子，同样是使用上面的数据，只不过这次采用dbscan算法

1
2
from sklearn.cluster import DBSCAN
db=DBSCAN(eps=1,min_samples=10)
1
2
#这次使用全部特征进行聚类
x=iris.data
1
x.shape
(150, 4)

1
db.fit(x)#训练数据集，构建模型
DBSCAN(algorithm='auto', eps=1, leaf_size=30, metric='euclidean',
    min_samples=10, n_jobs=1, p=None)

1
labels=db.labels_
1
labels
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

1
2
#噪声比率
ratio=len(labels[labels[:]==-1])*1.0/len(labels)
1
print "噪声比率:",ratio
噪声比率: 0.0

1
2
n_clusters_=len(set(labels)) - (1 if -1 in labels else 0)
print '聚类总数为：',n_clusters_
聚类总数为： 2

1
2
from sklearn import metrics
print '聚类效果评价指标：',metrics.silhouette_score(X,labels)#【-1,1】,越接近１越好
聚类效果评价指标： 0.766723428068

1
2
3
4
5
6
7
8
9
#总结
for i in range(n_clusters_):
    print 'Cluter',i+1,':'
    count=len(x[labels==i])
    mean=np.mean(x[labels==i][:,1])
    std=np.std(x[labels==i][:,1])
    print '计数：',count
    print '平均值',mean
    print '标准差',std
Cluter 1 :
计数： 50
平均值 3.418
标准差 0.377194909828
Cluter 2 :
计数： 100
平均值 2.872
标准差 0.331083071147

1
2
3
4
5
6
7
8
#可视化聚类结果，这里只选取前两个进行绘制,不太准确，只是拿来说明一下绘图做法
for i in range(n_clusters_):
    print '簇 ', i, '的所有样本:'
    one_cluster = x[labels == i]
    print one_cluster
    plt.plot(one_cluster[:,0],one_cluster[:,1],'o')

plt.show()
簇  0 的所有样本:
[[ 5.1  3.5  1.4  0.2]
 [ 4.9  3.   1.4  0.2]
 [ 4.7  3.2  1.3  0.2]
 [ 4.6  3.1  1.5  0.2]
 [ 5.   3.6  1.4  0.2]
 [ 5.4  3.9  1.7  0.4]
 [ 4.6  3.4  1.4  0.3]
 [ 5.   3.4  1.5  0.2]
 [ 4.4  2.9  1.4  0.2]
 [ 4.9  3.1  1.5  0.1]
 [ 5.4  3.7  1.5  0.2]
 [ 4.8  3.4  1.6  0.2]
 [ 4.8  3.   1.4  0.1]
 [ 4.3  3.   1.1  0.1]
 [ 5.8  4.   1.2  0.2]
 [ 5.7  4.4  1.5  0.4]
 [ 5.4  3.9  1.3  0.4]
 [ 5.1  3.5  1.4  0.3]
 [ 5.7  3.8  1.7  0.3]
 [ 5.1  3.8  1.5  0.3]
 [ 5.4  3.4  1.7  0.2]
 [ 5.1  3.7  1.5  0.4]
 [ 4.6  3.6  1.   0.2]
 [ 5.1  3.3  1.7  0.5]
 [ 4.8  3.4  1.9  0.2]
 [ 5.   3.   1.6  0.2]
 [ 5.   3.4  1.6  0.4]
 [ 5.2  3.5  1.5  0.2]
 [ 5.2  3.4  1.4  0.2]
 [ 4.7  3.2  1.6  0.2]
 [ 4.8  3.1  1.6  0.2]
 [ 5.4  3.4  1.5  0.4]
 [ 5.2  4.1  1.5  0.1]
 [ 5.5  4.2  1.4  0.2]
 [ 4.9  3.1  1.5  0.1]
 [ 5.   3.2  1.2  0.2]
 [ 5.5  3.5  1.3  0.2]
 [ 4.9  3.1  1.5  0.1]
 [ 4.4  3.   1.3  0.2]
 [ 5.1  3.4  1.5  0.2]
 [ 5.   3.5  1.3  0.3]
 [ 4.5  2.3  1.3  0.3]
 [ 4.4  3.2  1.3  0.2]
 [ 5.   3.5  1.6  0.6]
 [ 5.1  3.8  1.9  0.4]
 [ 4.8  3.   1.4  0.3]
 [ 5.1  3.8  1.6  0.2]
 [ 4.6  3.2  1.4  0.2]
 [ 5.3  3.7  1.5  0.2]
 [ 5.   3.3  1.4  0.2]]
簇  1 的所有样本:
[[ 7.   3.2  4.7  1.4]
 [ 6.4  3.2  4.5  1.5]
 [ 6.9  3.1  4.9  1.5]
 [ 5.5  2.3  4.   1.3]
 [ 6.5  2.8  4.6  1.5]
 [ 5.7  2.8  4.5  1.3]
 [ 6.3  3.3  4.7  1.6]
 [ 4.9  2.4  3.3  1. ]
 [ 6.6  2.9  4.6  1.3]
 [ 5.2  2.7  3.9  1.4]
 [ 5.   2.   3.5  1. ]
 [ 5.9  3.   4.2  1.5]
 [ 6.   2.2  4.   1. ]
 [ 6.1  2.9  4.7  1.4]
 [ 5.6  2.9  3.6  1.3]
 [ 6.7  3.1  4.4  1.4]
 [ 5.6  3.   4.5  1.5]
 [ 5.8  2.7  4.1  1. ]
 [ 6.2  2.2  4.5  1.5]
 [ 5.6  2.5  3.9  1.1]
 [ 5.9  3.2  4.8  1.8]
 [ 6.1  2.8  4.   1.3]
 [ 6.3  2.5  4.9  1.5]
 [ 6.1  2.8  4.7  1.2]
 [ 6.4  2.9  4.3  1.3]
 [ 6.6  3.   4.4  1.4]
 [ 6.8  2.8  4.8  1.4]
 [ 6.7  3.   5.   1.7]
 [ 6.   2.9  4.5  1.5]
 [ 5.7  2.6  3.5  1. ]
 [ 5.5  2.4  3.8  1.1]
 [ 5.5  2.4  3.7  1. ]
 [ 5.8  2.7  3.9  1.2]
 [ 6.   2.7  5.1  1.6]
 [ 5.4  3.   4.5  1.5]
 [ 6.   3.4  4.5  1.6]
 [ 6.7  3.1  4.7  1.5]
 [ 6.3  2.3  4.4  1.3]
 [ 5.6  3.   4.1  1.3]
 [ 5.5  2.5  4.   1.3]
 [ 5.5  2.6  4.4  1.2]
 [ 6.1  3.   4.6  1.4]
 [ 5.8  2.6  4.   1.2]
 [ 5.   2.3  3.3  1. ]
 [ 5.6  2.7  4.2  1.3]
 [ 5.7  3.   4.2  1.2]
 [ 5.7  2.9  4.2  1.3]
 [ 6.2  2.9  4.3  1.3]
 [ 5.1  2.5  3.   1.1]
 [ 5.7  2.8  4.1  1.3]
 [ 6.3  3.3  6.   2.5]
 [ 5.8  2.7  5.1  1.9]
 [ 7.1  3.   5.9  2.1]
 [ 6.3  2.9  5.6  1.8]
 [ 6.5  3.   5.8  2.2]
 [ 7.6  3.   6.6  2.1]
 [ 4.9  2.5  4.5  1.7]
 [ 7.3  2.9  6.3  1.8]
 [ 6.7  2.5  5.8  1.8]
 [ 7.2  3.6  6.1  2.5]
 [ 6.5  3.2  5.1  2. ]
 [ 6.4  2.7  5.3  1.9]
 [ 6.8  3.   5.5  2.1]
 [ 5.7  2.5  5.   2. ]
 [ 5.8  2.8  5.1  2.4]
 [ 6.4  3.2  5.3  2.3]
 [ 6.5  3.   5.5  1.8]
 [ 7.7  3.8  6.7  2.2]
 [ 7.7  2.6  6.9  2.3]
 [ 6.   2.2  5.   1.5]
 [ 6.9  3.2  5.7  2.3]
 [ 5.6  2.8  4.9  2. ]
 [ 7.7  2.8  6.7  2. ]
 [ 6.3  2.7  4.9  1.8]
 [ 6.7  3.3  5.7  2.1]
 [ 7.2  3.2  6.   1.8]
 [ 6.2  2.8  4.8  1.8]
 [ 6.1  3.   4.9  1.8]
 [ 6.4  2.8  5.6  2.1]
 [ 7.2  3.   5.8  1.6]
 [ 7.4  2.8  6.1  1.9]
 [ 7.9  3.8  6.4  2. ]
 [ 6.4  2.8  5.6  2.2]
 [ 6.3  2.8  5.1  1.5]
 [ 6.1  2.6  5.6  1.4]
 [ 7.7  3.   6.1  2.3]
 [ 6.3  3.4  5.6  2.4]
 [ 6.4  3.1  5.5  1.8]
 [ 6.   3.   4.8  1.8]
 [ 6.9  3.1  5.4  2.1]
 [ 6.7  3.1  5.6  2.4]
 [ 6.9  3.1  5.1  2.3]
 [ 5.8  2.7  5.1  1.9]
 [ 6.8  3.2  5.9  2.3]
 [ 6.7  3.3  5.7  2.5]
 [ 6.7  3.   5.2  2.3]
 [ 6.3  2.5  5.   1.9]
 [ 6.5  3.   5.2  2. ]
 [ 6.2  3.4  5.4  2.3]
 [ 5.9  3.   5.1  1.8]]

png

参考：
https://blog.csdn.net/luanpeng825485697/article/details/79443512
https://blog.csdn.net/linzch3/article/details/76038172
https://blog.csdn.net/u010159842/article/details/78624135

1
2


凡希 wechat
喜欢所以热爱，坚持干货分享，欢迎订阅我的微信公众号
呐，请我吃辣条