Clustering the 3D points when given the x,y,z coordinates using DBSCAN algorithm using python

Question

I'm trying to cluster some 3D points with the help of some given coordinates using DBSCAN algorithm with python.

ex:- given coordinates will be like follows

  X      Y      Z

 [-37.530  3.109  -16.452]
 [40.247  5.483  -15.209]
 [-31.920 12.584  -12.916] 
 [-32.760 14.072  -13.749]
 [-37.100  1.953  -15.720] 
 [-32.143 12.990  -13.488]
 [-41.077  4.651  -15.651] 
 [-34.219 13.611  -13.090]
 [-33.117 15.875  -13.738]  e.t.c

I'm kind of new to programming and searching for an example script how to write the codes. Can some one give a suggestion or an example? Thanks a lot in advance.

Answer 1

You can use sklearn.cluster.DBSCAN. In your case:

import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import DBSCAN

data = np.array([[-37.530, 3.109, -16.452],
                [40.247, 5.483, -15.209],
                [-31.920, 12.584, -12.916],
                [-32.760, 14.072, -13.749],
                [-37.100, 1.953, -15.720],
                [-32.143, 12.990, -13.488],
                [-41.077, 4.651, -15.651], 
                [-34.219, 13.611, -13.090],
                [-33.117, 15.875, -13.738]])

fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(data[:,0], data[:,1], data[:,2], s=300)
ax.view_init(azim=200)
plt.show()

model = DBSCAN(eps=2.5, min_samples=2)
model.fit_predict(data)
pred = model.fit_predict(data)

fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(data[:,0], data[:,1], data[:,2], c=model.labels_, s=300)
ax.view_init(azim=200)
plt.show()

print("number of cluster found: {}".format(len(set(model.labels_))))
print('cluster for each point: ', model.labels_)

ouput

• before clustering

• after clustering

number of cluster found: 3
cluster for each point:  [ 0 -1  1  1  0  1 -1  1  1]