beer数据集 聚类分析 import pandas as pd
beer = pd.read_csv('./data/data.txt', sep=' ')
print(beer)
name calories sodium alcohol cost
0 Budweiser 144 15 4.7 0.43
1 Schlitz 151 19 4.9 0.43
2 Lowenbrau 157 15 0.9 0.48
3 Kronenbourg 170 7 5.2 0.73
4 Heineken 152 11 5.0 0.77
5 Old_Milwaukee 145 23 4.6 0.28
6 Augsberger 175 24 5.5 0.40
7 Srohs_Bohemian_Style 149 27 4.7 0.42
8 Miller_Lite 99 10 4.3 0.43
9 Budweiser_Light 113 8 3.7 0.40
10 Coors 140 18 4.6 0.44
11 Coors_Light 102 15 4.1 0.46
12 Michelob_Light 135 11 4.2 0.50
13 Becks 150 19 4.7 0.76
14 Kirin 149 6 5.0 0.79
15 Pabst_Extra_Light 68 15 2.3 0.38
16 Hamms 139 19 4.4 0.43
17 Heilemans_Old_Style 144 24 4.9 0.43
18 Olympia_Goled_Light 72 6 2.9 0.46
19 Schlitz_Light 97 7 4.2 0.47
K-means聚类算法 from sklearn.cluster import KMeans
km = KMeans(n_clusters=3).fit(X)
km2 = KMeans(n_clusters=2).fit(X)
km.labels_
array([0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 1, 0, 0, 1, 2])
beer['cluster'] = km.labels_
beer['cluster2'] = km2.labels_
beer.sort_values('cluster')
name calories sodium alcohol cost cluster cluster2 Budweiser 144 15 4.7 0.43 1 Schlitz 151 19 4.9 0.43 2 Lowenbrau 157 15 0.9 0.48 3 Kronenbourg 170 7 5.2 0.73 4 Heineken 152 11 5.0 0.77 5 Old_Milwaukee 145 23 4.6 0.28 6 Augsberger 175 24 5.5 0.40 7 Srohs_Bohemian_Style 149 27 4.7 0.42 17 Heilemans_Old_Style 144 24 4.9 0.43 10 Coors 140 18 4.6 0.44 16 Hamms 139 19 4.4 0.43 12 Michelob_Light 135 11 4.2 0.50 13 Becks 150 19 4.7 0.76 14 Kirin 149 6 5.0 0.79 18 Olympia_Goled_Light 72 6 2.9 0.46 1 1 15 Pabst_Extra_Light 68 15 2.3 0.38 1 1 9 Budweiser_Light 113 8 3.7 0.40 2 1 8 Miller_Lite 99 10 4.3 0.43 2 1 11 Coors_Light 102 15 4.1 0.46 2 1 19 Schlitz_Light 97 7 4.2 0.47 2 1
from pandas.tools.plotting import scatter_matrix
%matplotlib inline
cluster_centers = km.cluster_centers_
cluster2_centers = km2.cluster_centers_
calories sodium alcohol cost cluster2 cluster 150.00 17.0 4.521429 0.520714 1 70.00 10.5 2.600000 0.420000 1 2 102.75 10.0 4.075000 0.440000 1
calories sodium alcohol cost cluster cluster2 150.000000 17.000000 4.521429 0.520714 0.000000 1 91.833333 10.166667 3.583333 0.433333 1.666667
import matplotlib.pyplot as plt
plt.rcParams['font.size'] = 14
import numpy as np
colors = np.array(['red', 'green', 'blue', 'yellow'])
plt.scatter(beer['calories'], beer['alcohol'], c = colors[beer['cluster']])
plt.scatter(centers.calories, centers.alcohol, linewidths=3, marker='+', s=300, c='black')
plt.xlabel('calories')
plt.ylabel('alcohol')
Text(0, 0.5, 'alcohol')
7-2基于Kmeans和DBSCAN算法的啤酒成分分析实战 scatter_matrix(beer[["calories","sodium","alcohol","cost"]],s=100, alpha=1, c=colors[beer["cluster"]], figsize=(10,10))
plt.suptitle("With 3 centroids initialized")
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.
"""Entry point for launching an IPython kernel.
Text(0.5, 0.98, 'With 3 centroids initialized')
7-2基于Kmeans和DBSCAN算法的啤酒成分分析实战 scatter_matrix(beer[['calories','sodium','alcohol','cost']],s=100, alpha=1, c=colors[beer['cluster2']], figsize=(10,10))
plt.suptitle('With 2 centroids initialized')
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.
"""Entry point for launching an IPython kernel.
Text(0.5, 0.98, 'With 2 centroids initialized')
7-2基于Kmeans和DBSCAN算法的啤酒成分分析实战 数据标准化或归一化 from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_scaled
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.
return self.partial_fit(X, y)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.
return self.fit(X, **fit_params).transform(X)
array([[ 0.38791334, 0.00779468, 0.43380786, -0.45682969],
[ 0.6250656 , 0.63136906, 0.62241997, -0.45682969],
[ 0.82833896, 0.00779468, -3.14982226, -0.10269815],
[ 1.26876459, -1.23935408, 0.90533814, 1.66795955],
[ 0.65894449, -0.6157797 , 0.71672602, 1.95126478],
[ 0.42179223, 1.25494344, 0.3395018 , -1.5192243 ],
[ 1.43815906, 1.41083704, 1.1882563 , -0.66930861],
[ 0.55730781, 1.87851782, 0.43380786, -0.52765599],
[-1.1366369 , -0.7716733 , 0.05658363, -0.45682969],
[-0.66233238, -1.08346049, -0.5092527 , -0.66930861],
[ 0.25239776, 0.47547547, 0.3395018 , -0.38600338],
[-1.03500022, 0.00779468, -0.13202848, -0.24435076],
[ 0.08300329, -0.6157797 , -0.03772242, 0.03895447],
[ 0.59118671, 0.63136906, 0.43380786, 1.88043848],
[ 0.55730781, -1.39524768, 0.71672602, 2.0929174 ],
[-2.18688263, 0.00779468, -1.82953748, -0.81096123],
[ 0.21851887, 0.63136906, 0.15088969, -0.45682969],
[ 0.38791334, 1.41083704, 0.62241997, -0.45682969],
[-2.05136705, -1.39524768, -1.26370115, -0.24435076],
[-1.20439469, -1.23935408, -0.03772242, -0.17352445]])
beer['scaled_cluster'] = km.labels_
beer.sort_values('scaled_cluster')
name calories sodium alcohol cost cluster cluster2 scaled_cluster Budweiser 144 15 4.7 0.43 1 Schlitz 151 19 4.9 0.43 17 Heilemans_Old_Style 144 24 4.9 0.43 16 Hamms 139 19 4.4 0.43 5 Old_Milwaukee 145 23 4.6 0.28 6 Augsberger 175 24 5.5 0.40 7 Srohs_Bohemian_Style 149 27 4.7 0.42 10 Coors 140 18 4.6 0.44 15 Pabst_Extra_Light 68 15 2.3 0.38 1 1 1 12 Michelob_Light 135 11 4.2 0.50 1 11 Coors_Light 102 15 4.1 0.46 2 1 1 9 Budweiser_Light 113 8 3.7 0.40 2 1 1 8 Miller_Lite 99 10 4.3 0.43 2 1 1 2 Lowenbrau 157 15 0.9 0.48 1 18 Olympia_Goled_Light 72 6 2.9 0.46 1 1 1 19 Schlitz_Light 97 7 4.2 0.47 2 1 1 13 Becks 150 19 4.7 0.76 2 14 Kirin 149 6 5.0 0.79 2 4 Heineken 152 11 5.0 0.77 2 3 Kronenbourg 170 7 5.2 0.73 2
calories sodium alcohol cost cluster cluster2 scaled_cluster 148.375 21.125 4.7875 0.4075 0.00 0.00 1 105.375 10.875 3.3250 0.4475 1.25 0.75 2 155.250 10.750 4.9750 0.7625 0.00 0.00
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: pandas.scatter_matrix is deprecated, use pandas.plotting.scatter_matrix instead
"""Entry point for launching an IPython kernel.
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010717630>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000102F2668>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FFDE8D0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FFCFD68>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001072F208>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010729470>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000105FA6D8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010690DD8>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010690E10>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000106242B0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010330518>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000001033B780>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000105A89E8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000102A0C50>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000102BEEB8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010574160>]],
dtype=object)
7-2基于Kmeans和DBSCAN算法的啤酒成分分析实战 聚类评估:轮廓系数(Silhouette Coefficient ) 7-2基于Kmeans和DBSCAN算法的啤酒成分分析实战 ![在这里插入图片描述](https://img-blog.csdnimg.cn/20190318174226115.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjYwMDA3Mg==,size_16,color_FFFFFF,t_70) - 计算样本i到同簇其他样本的平均距离ai。ai 越小,说明样本i越应该被聚类到该簇。将ai 称为样本i的簇内不相似度。 - 计算样本i到其他某簇Cj 的所有样本的平均距离bij,称为样本i与簇Cj 的不相似度。定义为样本i的簇间不相似度:bi =min{bi1, bi2, ..., bik}
si接近1,则说明样本i聚类合理 si接近-1,则说明样本i更应该分类到另外的簇 若si 近似为0,则说明样本i在两个簇的边界上。 from sklearn import metrics
score = metrics.silhouette_score(X,beer.cluster)
score_scaled = metrics.silhouette_score(X, beer.scaled_cluster)
print(score, score_scaled)
0.6731775046455796 0.1797806808940007
scores = []
for k in range(2,20):
labels = KMeans(n_clusters=k).fit(X).labels_
score = metrics.silhouette_score(X, labels)
scores.append(score)
scores
[0.6917656034079486,
0.6731775046455796,
0.5857040721127795,
0.4225487335172022,
0.4559182167013378,
0.43776116697963136,
0.38946337473126,
0.39746405172426014,
0.3915697409245163,
0.3413109618039333,
0.3459775237127248,
0.31221439248428434,
0.30707782144770296,
0.31834561839139497,
0.2849514001174898,
0.23498077333071996,
0.1588091017496281,
0.08423051380151177]
plt.plot(list(range(2,20)), scores)
plt.xlabel("Number of Clusters Initialized")
plt.ylabel("Sihouette Score")
Text(0, 0.5, 'Sihouette Score')
7-2基于Kmeans和DBSCAN算法的啤酒成分分析实战 DBSCAN聚类 from sklearn.cluster import DBSCAN
db = DBSCAN(eps=10, min_samples=2).fit(X) #半径为10,密度为2
labels = db.labels_
beer['cluster_db'] = labels
beer.sort_values('cluster_db')
name calories sodium alcohol cost cluster cluster2 scaled_cluster cluster_db 9 Budweiser_Light 113 8 3.7 0.40 2 1 1 -1 3 Kronenbourg 170 7 5.2 0.73 2 -1 6 Augsberger 175 24 5.5 0.40 -1 17 Heilemans_Old_Style 144 24 4.9 0.43 16 Hamms 139 19 4.4 0.43 14 Kirin 149 6 5.0 0.79 2 13 Becks 150 19 4.7 0.76 2 12 Michelob_Light 135 11 4.2 0.50 1 10 Coors 140 18 4.6 0.44 Budweiser 144 15 4.7 0.43 7 Srohs_Bohemian_Style 149 27 4.7 0.42 5 Old_Milwaukee 145 23 4.6 0.28 4 Heineken 152 11 5.0 0.77 2 2 Lowenbrau 157 15 0.9 0.48 1 1 Schlitz 151 19 4.9 0.43 8 Miller_Lite 99 10 4.3 0.43 2 1 1 1 11 Coors_Light 102 15 4.1 0.46 2 1 1 1 19 Schlitz_Light 97 7 4.2 0.47 2 1 1 1 15 Pabst_Extra_Light 68 15 2.3 0.38 1 1 1 2 18 Olympia_Goled_Light 72 6 2.9 0.46 1 1 1 2
calories sodium alcohol cost cluster cluster2 scaled_cluster cluster_db -1 152.666667 13.000000 4.800000 0.510000 0.666667 0.333333 1.000000 146.250000 17.250000 4.383333 0.513333 0.000000 0.000000 0.666667 1 99.333333 10.666667 4.200000 0.453333 2.000000 1.000000 1.000000 2 70.000000 10.500000 2.600000 0.420000 1.000000 1.000000 1.000000
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: pandas.scatter_matrix is deprecated, use pandas.plotting.scatter_matrix instead
"""Entry point for launching an IPython kernel.
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000107B6F98>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010542C18>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000001042E278>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000107C0208>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000109376D8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010984940>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000012022BA8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000001206C438>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001206C470>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011F848D0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011F04B38>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000012093DA0>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000121A6048>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011F672B0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000120E5518>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000107AA780>]],
dtype=object)
7-2基于Kmeans和DBSCAN算法的啤酒成分分析实战