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K-means clustering is a method for finding clusters and cluster centers in a set of unlabelled data.

cluster means group of matching data ,we can show using different label for example we can create three different sub-group for red ,green and blue to manage related data ,if item will be belonging from red color then it will be the part of red cluster.

step for clustering:-

1) prepare data using repository or from array using numpy


2)  if we want to re scale data then we can use whiten()


3)  calculate centroid point from data based on number of cluster.

4)  display possible matching from cluster with values it will return the minimum difference using 0,1 and 2 ... form


Complete code of K-means Clustering algorithm ,it will be mainly implemented in ML:-

Complete example of Clustering concept

from numpy import hstack,array
from numpy.random import rand
from scipy.cluster.vq import *
data = vstack((rand(10,3) + array([1,1,1]),rand(10,3)))
centroids,_ = kmeans(data,5)
print(centroids)
clx,_ = vq(data,centroids)
print(clx) 




                                   

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