机器学习实战（四）—密度聚类算法DBSCAN

DBSCAN（Density-Based Spatial Clustering of Applications with Noise）是一个出现得比较早（1996年），比较有代表性的基于密度的聚类算法。算法的主要目标是相比基于划分的聚类方法和层次聚类方法，需要更少的领域知识来确定输入参数；发现任意形状的聚簇；在大规模数据库上更好的效率。DBSCAN能够将足够高密度的区域划分成簇，并能在具有噪声的空间数据库中发现任意形状的簇。

算法流程
机器学习实战（四）—密度聚类算法DBSCAN
实例—学生月上网时间分布聚类分析

import numpy as np
import sklearn.cluster as skc
from sklearn import metrics
import matplotlib.pyplot as plt
 
 
mac2id=dict()
onlinetimes=[]
f=open('TestData.txt',encoding='utf-8')
for line in f:
    mac=line.split(',')[2]
    onlinetime=int(line.split(',')[6])
    starttime=int(line.split(',')[4].split(' ')[1].split(':')[0])
    if mac not in mac2id:
        mac2id[mac]=len(onlinetimes)
        onlinetimes.append((starttime,onlinetime))
    else:
        onlinetimes[mac2id[mac]]=[(starttime,onlinetime)]
real_X=np.array(onlinetimes).reshape((-1,2))
 
X=real_X[:,0:1]
 
db=skc.DBSCAN(eps=0.01,min_samples=20).fit(X)
labels = db.labels_
 
print('Labels:')
print(labels)
raito=len(labels[labels[:] == -1]) / len(labels)
print('Noise raito:',format(raito, '.2%'))
 
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
 
print('Estimated number of clusters: %d' % n_clusters_)
print("Silhouette Coefficient: %0.3f"% metrics.silhouette_score(X, labels))
 
for i in range(n_clusters_):
    print('Cluster ',i,':')
    print(list(X[labels == i].flatten()))
     
plt.hist(X,24)

输入：

In [10]: run Dbscan.py
Labels:
[ 0 -1  0  1 -1  1  0  1  2 -1  1  0  1  1  3 -1 -1  3 -1  1  1 -1  1  3
  4 -1  1  1  2  0  2  2 -1  0  1  0  0  0  1  3 -1  0  1  1  0  0  2 -1
  1  3  1 -1  3 -1  3  0  1  1  2  3  3 -1 -1 -1  0  1  2  1 -1  3  1  1
  2  3  0  1 -1  2  0  0  3  2  0  1 -1  1  3 -1  4  2 -1 -1  0 -1  3 -1
  0  2  1 -1 -1  2  1  1  2  0  2  1  1  3  3  0  1  2  0  1  0 -1  1  1
  3 -1  2  1  3  1  1  1  2 -1  5 -1  1  3 -1  0  1  0  0  1 -1 -1 -1  2
  2  0  1  1  3  0  0  0  1  4  4 -1 -1 -1 -1  4 -1  4  4 -1  4 -1  1  2
  2  3  0  1  0 -1  1  0  0  1 -1 -1  0  2  1  0  2 -1  1  1 -1 -1  0  1
  1 -1  3  1  1 -1  1  1  0  0 -1  0 -1  0  0  2 -1  1 -1  1  0 -1  2  1
  3  1  1 -1  1  0  0 -1  0  0  3  2  0  0  5 -1  3  2 -1  5  4  4  4 -1
  5  5 -1  4  0  4  4  4  5  4  4  5  5  0  5  4 -1  4  5  5  5  1  5  5
  0  5  4  4 -1  4  4  5  4  0  5  4 -1  0  5  5  5 -1  4  5  5  5  5  4
  4]
Noise raito: 22.15%
Estimated number of clusters: 6
Silhouette Coefficient: 0.710
Cluster  0 :
[22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22]
Cluster  1 :
[23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23]
Cluster  2 :
[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]
Cluster  3 :
[21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21]
Cluster  4 :
[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]
Cluster  5 :
[7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]