Python: DBSCAN in 3 dimensional space

python cluster-analysis dbscan
user2909415 picture user2909415 · Oct 7, 2014 · Viewed 11.9k times · Source

I have been searching around for an implementation of DBSCAN for 3 dimensional points without much luck. Does anyone know I library that handles this or has any experience with doing this? I am assuming that the DBSCAN algorithm can handle 3 dimensions, by having the e value be a radius metric and the distance between points measured by euclidean separation. If anyone has tried implementing this and would like to share that would also be greatly appreciated, thanks.

Answer

mayTheCodeBeWithYou picture mayTheCodeBeWithYou · Feb 14, 2015

You can use sklearn for DBSCAN. Here is some code that works for me-

from sklearn.cluster import DBSCAN
import numpy as np
data = np.random.rand(500,3)

db = DBSCAN(eps=0.12, min_samples=1).fit(data)
labels = db.labels_
from collections import Counter
Counter(labels)

The output I got was-

Counter({1: 342, 10: 30, 31: 13, 13: 11, 30: 10, 24: 5, 29: 5, 2: 4, 18: 4,
19: 4, 28: 4, 49: 4, 3: 3, 17: 3, 23: 3, 32: 3, 7: 2, 9: 2, 12: 2, 14: 2, 15: 2,
16: 2, 20: 2, 21: 2, 26: 2, 39: 2, 41: 2, 46: 2, 0: 1, 4: 1, 5: 1, 6: 1, 8: 1, 11:
1, 22: 1, 25: 1, 27: 1, 33: 1, 34: 1, 35: 1, 36: 1, 37: 1, 38: 1, 40: 1, 42: 1,
43: 1, 44: 1, 45: 1, 47: 1, 48: 1, 50: 1, 51: 1, 52: 1, 53: 1, 54: 1, 55: 1})

So, the clustering identifies 55 clusters with the count of the number of points in each cluster as shown above.

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