I'm quite new to scikit learn, but wanted to try an interesting project.
I have longitude and latitudes for points in the UK, which I used to create cluster centers using scikit learns KMeans class. To visualise this data, rather than having the points as clusters, I wanted to instead draw boundaries around each cluster. For example, if one cluster was London and the other Oxford, I currently have a point at the center of each city, but I was wondering if there's a way to use this data to create a boundary line based on my clusters instead?
Here is my code so far to create the clusters:
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
location1="XXX"
df = pd.read_csv(location1, encoding = "ISO-8859-1")
#Run kmeans clustering
X = df[['long','lat']].values #~2k locations in the UK
y=df['label'].values #Label is a 0 or 1
kmeans = KMeans(n_clusters=30, random_state=0).fit(X, y)
centers=kmeans.cluster_centers_
plt.scatter(centers[:,0],centers[:,1], marker='s', s=100)
So I would like to be able to convert the centers in the above example to lines that demarcate each of the regions -- is this possible?
Thanks,
Anant
I guess you're talking about spatial boundaries, in this case you should follow Bunyk's recommendation and use a Voronoi Diagram [ 1 ]. Here is a practical demonstration of what you could achieve: http://nbviewer.jupyter.org/gist/pv/8037100 .
You can use Scipi to generate a Voronoi Diagram. docs
For your code it would be
from scipy.spatial import Voronoi, voronoi_plot_2d
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
location1="XXX"
df = pd.read_csv(location1, encoding = "ISO-8859-1")
#Run kmeans clustering
X = df[['long','lat']].values #~2k locations in the UK
y=df['label'].values #Label is a 0 or 1
kmeans = KMeans(n_clusters=30, random_state=0).fit(X, y)
centers=kmeans.cluster_centers_
plt.scatter(centers[:,0],centers[:,1], marker='s', s=100)
vor = Voronoi(centers)
fig = voronoi_plot_2d(vor,plt.gca())
plt.show()
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