Chapter 4 - Clustering Models

Segment 3 - DBSCan clustering to identify outliers

DBSCAN for Outlier Detection

• Unsupervised method that clusters core samples(dense areas of a dataset) and denotes non-core samples(sparse portions of the dataset)
• Use to identify collective outliers
• Outliers should make up ≤5% of the total observations. Adjust model parameters accordingly

Important DBSCAN model parameters:

• eps
• min_samples

import pandas as pd

import matplotlib.pyplot as plt
from pylab import rcParams
import seaborn as sb

import sklearn
from sklearn.cluster import DBSCAN
from collections import Counter

%matplotlib inline
rcParams['figure.figsize'] = 5, 4
sb.set_style('whitegrid')

DBSCan clustering to identify outliers

Train your model and identify outliers

# with this example, we're going to use the same data that we used for the rest of this chapter. So we're going to copy and 
# paste in the code. 
address = '~/Data/iris.data.csv'
df = pd.read_csv(address, header=None, sep=',')

df.columns=['Sepal Length','Sepal Width','Petal Length','Petal Width', 'Species']

data = df.iloc[:,0:4].values
target = df.iloc[:,4].values

df[:5]

model = DBSCAN(eps=0.8, min_samples=19).fit(data)
print(model)

DBSCAN(eps=0.8, min_samples=19)

Visualize your results

outliers_df = pd.DataFrame(data)

print(Counter(model.labels_))

print(outliers_df[model.labels_==-1])

Counter({1: 94, 0: 50, -1: 6})
       0    1    2    3
98   5.1  2.5  3.0  1.1
105  7.6  3.0  6.6  2.1
117  7.7  3.8  6.7  2.2
118  7.7  2.6  6.9  2.3
122  7.7  2.8  6.7  2.0
131  7.9  3.8  6.4  2.0

fig = plt.figure()
ax = fig.add_axes([.1,.1,1,1])

colors = model.labels_

ax.scatter(data[:,2],data[:,1],c=colors,s=120)
ax.set_xlabel('Petal Length')
ax.set_ylabel('Sepal Width')
plt.title('DBSCAN for Outlier Detection')

Text(0.5, 1.0, 'DBSCAN for Outlier Detection')

标签：clustering,DBSCan,Python,df,DBSCAN,import,model,outliers,data
来源： https://www.cnblogs.com/keepmoving1113/p/14320912.html