各种聚类算法的使用对比

1. 导入数据

# beer dataset
import pandas as pd
beer = pd.read_csv('data.txt', sep=' ')
beer

2.构建标签

X = beer[["calories","sodium","alcohol","cost"]]

3.使用聚类算法

K-means clustering：

from sklearn.cluster import KMeans

km = KMeans(n_clusters=3).fit(X)
km2 = KMeans(n_clusters=2).fit(X)

km.labels_

beer['cluster'] = km.labels_
beer['cluster2'] = km2.labels_
beer.sort_values('cluster')

from pandas.plotting import scatter_matrix
%matplotlib inline

cluster_centers = km.cluster_centers_

cluster_centers_2 = km2.cluster_centers_

beer.groupby("cluster").mean()

beer.groupby("cluster2").mean()

centers = beer.groupby("cluster").mean().reset_index()

%matplotlib inline
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")

scatter_matrix(beer[["calories","sodium","alcohol","cost"]],s=100, alpha=1, c=colors[beer["cluster"]], figsize=(10,10))
plt.suptitle("With 3 centroids initialized")

注释

scatter_matrix(frame, alpha=0.5, c,figsize=None, ax=None, diagonal='hist', marker='.', density_kwds=None,hist_kwds=None, range_padding=0.05, **kwds)

参数含义

1. frame:pandas dataframe对象；
2. alpha:图像透明度
3. figsize:以英寸为单位的图像大小，一般以元组 (width, height) 形式设置
4. ax:可选一般为none
5. diagonal:必须且只能在{‘hist’, ‘kde’}中选择1个，’hist’表示直方图(Histogram plot),’kde’表示核密度估计(Kernel Density Estimation)；该参数是scatter_matrix函数的关键参数
6. marker:Matplotlib可用的标记类型，如’.’，’,’，’o’等
7. density_kwds:(other plotting keyword arguments，可选)，与kde相关的字典参数
8. hist_kwds:与hist相关的字典参数
9. range_padding:(float, 可选)，图像在x轴、y轴原点附近的留白(padding)，该值越大，留白距离越大，图像远离坐标原点
10. kwds:与scatter_matrix函数本身相关的字典参数
    

scatter_matrix(beer[["calories","sodium","alcohol","cost"]],s=100, alpha=1, c=colors[beer["cluster2"]], figsize=(10,10))
plt.suptitle("With 2 centroids initialized")

Scaled data

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_scaled

km = KMeans(n_clusters=3).fit(X_scaled)

beer["scaled_cluster"] = km.labels_
beer.sort_values("scaled_cluster")

beer.groupby("scaled_cluster").mean()

pd.plotting.scatter_matrix(X, c=colors[beer.scaled_cluster], alpha=1, figsize=(10,10), s=100)

聚类评估：轮廓系数（Silhouette Coefficient ）

$s(i)=\frac{b(i)-a(i)}{max\{a(i),b(i)\}},s(i)=\begin{Bmatrix} 1-\frac{a(i)}{b(i)} & ,a(i)<b(i)\\ 0 & ,a(i)=b(i)\\ \frac{b(i)}{a(i)}-1 & ,a(i)>b(i) \end{Bmatrix}$

• 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_scaled = metrics.silhouette_score(X,beer.scaled_cluster)
score = metrics.silhouette_score(X,beer.cluster)
print(score_scaled, score)

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

plt.plot(list(range(2,20)), scores)
plt.xlabel("Number of Clusters Initialized")
plt.ylabel("Sihouette Score")

DBSCAN clustering

from sklearn.cluster import DBSCAN
db = DBSCAN(eps=10, min_samples=2).fit(X)

labels = db.labels_

beer['cluster_db'] = labels
beer.sort_values('cluster_db')

beer.groupby('cluster_db').mean()

pd.plotting.scatter_matrix(X, c=colors[beer.cluster_db], figsize=(10,10), s=100)

来源：CSDN

作者：关关雎鸠儿

链接：https://blog.csdn.net/weixin_42260102/article/details/104108131