CODEWITHSUNDEEP
pythonmachine-learningcluster-computingdata-analysis
Siddharth Shah
Siddharth Shah

Reputation: 123

Determining the size of cluster after Kmeans in Python

So I have successfully found out the optimal number of clusters required for kmeans algorithm in python, but now how can I find out the exact size of cluster that I get after applying the Kmeans in python?

Here's a code snippet

data=np.vstack(zip(simpleassetid_arr,simpleuidarr))
centroids,_ = kmeans(data,round(math.sqrt(len(uidarr)/2)))
idx,_ = vq(data,centroids)

initial = [cluster.vq.kmeans(data,i) for i in range(1,10)]
var=[var for (cent,var) in initial] #to determine the optimal number of k   using elbow test
num_k=int(raw_input("Enter the number of clusters: "))

cent, var = initial[num_k-1]

assignment,cdist = cluster.vq.vq(data,cent)

Upvotes: 3

Views: 7503

Answers (1)

Alex
Alex

Reputation: 21766

You can get the cluster size using this:

print np.bincount(idx)

For the the example below, np.bincount(idx) outputs an array of two elements, e.g. [ 156 144]

from numpy import vstack,array
import numpy as np
from numpy.random import rand
from scipy.cluster.vq import kmeans,vq
# data generation
data = vstack((rand(150,2) + array([.5,.5]),rand(150,2)))
# computing K-Means with K = 2 (2 clusters)
centroids,_ = kmeans(data,2)
# assign each sample to a cluster
idx,_ = vq(data,centroids)

#Print number of elements per cluster
print np.bincount(idx)

Upvotes: 3

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