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

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

K-means clustering

from sklearn.cluster import KMeans

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

km.labels_

输出 ： array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 2, 0, 0, 2, 1])

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

from pandas.tools.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")

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

输出 ：
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]])

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

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

What are the "characteristics" of each cluster?

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

pd.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 ）

• 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)

输出 ： 0.179780680894 0.673177504646

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

输出 ：
[0.69176560340794857,
0.67317750464557957,
0.58570407211277953,
0.42254873351720201,
0.4559182167013377,
0.43776116697963124,
0.38946337473125997,
0.39746405172426014,
0.33061511213823314,
0.34131096180393328,
0.34597752371272478,
0.31221439248428434,
0.30707782144770296,
0.31834561839139497,
0.28495140011748982,
0.23498077333071996,
0.15880910174962809,
0.084230513801511767]

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