I would like to use the knn distance plot to be able to figure out which eps value should I choose for the DBSCAN algorithm. Based on this page:
The idea is to calculate, the average of the distances of every point to its k nearest neighbors. The value of k will be specified by the user and corresponds to MinPts. Next, these k-distances are plotted in an ascending order. The aim is to determine the “knee”, which corresponds to the optimal eps parameter.
Using python with numpy/sklearn, I have the following points, with the following distance for 6-knn:
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
nbrs = NearestNeighbors(n_neighbors=len(X)).fit(X)
distances, indices = nbrs.kneighbors(X)
# Indices
[[0 1 2 3 4 5]
[1 0 2 3 4 5]
[2 1 0 3 4 5]
[3 4 5 0 1 2]
[4 3 5 0 1 2]
[5 4 3 0 1 2]]
# Distances
[[ 0. 1. 2.23606798 2.82842712 3.60555128 5. ]
[ 0. 1. 1.41421356 3.60555128 4.47213595 5.83095189]
[ 0. 1.41421356 2.23606798 5. 5.83095189 7.21110255]
[ 0. 1. 2.23606798 2.82842712 3.60555128 5. ]
[ 0. 1. 1.41421356 3.60555128 4.47213595 5.83095189]
[ 0. 1.41421356 2.23606798 5. 5.83095189 7.21110255]]
then I computed the average distance:
distances.mean()
2.9269575028354495
The problem is I don't understand how exactly could I represent the same plot as them with distances in y-axis and number of points according to the distances on the x-axis using python.
Thank for your help.