Unsupervised clustering with unknown number of clusters

Question

I have a large set of vectors in 3 dimensions. I need to cluster these based on Euclidean distance such that all the vectors in any particular cluster have a Euclidean dista

Answer 1

I want to add to moooeeeep's answer by using hierarchical clustering. This solution work for me, though it quite "random" to pick threshold value. By referrence to other source and test by myself, I got better method and threshold could be easily picked by dendrogram:

from scipy.cluster import hierarchy
from scipy.spatial.distance import pdist
import matplotlib.pyplot as plt

ori_array = ["Your_list_here"]
ward_array = hierarchy.ward(pdist(ori_array))
dendrogram = hierarchy.dendrogram(hierarchy.linkage(ori_array, method  = "ward"))
plt.title('Dendrogram')
plt.xlabel('Customers')
plt.ylabel('Euclidean distances')
plt.show()

You will see the plot like this click here. Then by drawing the horizontal line, let say at distance = 1, the number of conjunctions will be your desire number of clusters. So here I choose threshold = 1 for 4 clusters.

threshold = 1
clusters_list = hierarchy.fcluster(ward_array, threshold, criterion="distance")
print("Clustering list: {}".format(clusters_list))

Now each value in cluster_list will be an assigned cluster-id of the corresponding point in ori_array.