k means clustering algorithm python example

k means clustering algorithm python example

K Means Clustering is unsupervised learning algorithm in python (i.e.it's tries to cluster the different data based on their similarity) and another meaning is that there is no outcome to be predicted data. K Means Clustering algorithm just tries to find patterns in the data.
There are 3 steps for K Means Clustering with Python:
  • # step1: Initialisation – K initial means centroid in python are generated at random
  • # step2: Assignment – K clusters are created by observation with the nearest centroids data
  • # step3: Update – It's becomes the new mean
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 Initialisation – K initial means centroide in python are generated at random

Import Libraries
import seaborn as ns
import matplotlib.pyplot as plt
%matplotlib inline
Create some Data

from sklearn.datasets import make_blobs

Assignment – K clusters are created by observation with the nearest centroides data


data = make_blobs(n_samples=190, n_features=2, 
                           centers=4, cluster_std=1.7,random_state=100)
Visualize Data
plt.scatter(data[0][:,0],data[0][:,1],c=data[1],cmap='rainbow')
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k means clustering algorithm python example
k means clustering algorithm python example


from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=4)
kmeans.fit(data[0])
KMeans(copy_x=True, init='k-means++', max_iter=200, n_clusters=4, n_init=9,
    n_jobs=1, precompute_distances='auto', random_state=None, tol=0.001,
    verbose=0)

Update – It's becomes the new mean


kmeans.cluster_centers_
array([[-3.13591321,  6.95389851],
       [-7.46941837, -5.56081545],
       [-1.0123077 ,  3.13407664],
       [ 2.71749226,  6.01388735]])
kmeans.labels_
array([1, 3, 1, 3, 3, 0, 2, 1, 3, 1, 2, 1, 3, 3, 2, 1, 3, 1, 0, 2, 2, 1, 1,
       1, 1, 0, 0, 1, 3, 3, 2, 0, 3, 1, 1, 2, 0, 1, 0, 1, 0, 2, 2, 1, 1, 1,
       1, 1, 0, 1, 1, 2, 3, 1, 0, 2, 1, 1, 2, 3, 0, 3, 0, 2, 3, 1, 0, 3, 1,
       3, 3, 1, 0, 1, 0, 3, 3, 1, 2, 1, 1, 0, 3, 0, 1, 1, 1, 2, 1, 0, 0, 1,
       3, 1, 1, 0, 3, 2, 0, 3, 1, 0, 1, 1, 3, 1, 0, 3, 0, 0, 3, 2, 2, 3, 2,
       2, 2, 2, 3, 2, 1, 2, 1, 2, 1, 3, 2, 1, 0, 2, 2, 2, 1, 0, 0, 2, 3, 2,
       2, 1, 0, 3, 0, 2, 2, 3, 1, 0, 2, 2, 2, 2, 1, 3, 1, 2, 3, 3, 3, 1, 1,
       2, 1, 2, 0, 2, 1, 3, 2, 1, 3, 1, 2, 3, 1, 2, 3, 3, 0, 3, 2, 0, 0, 1,
       1, 0, 3, 0, 0, 1, 3, 3, 3, 2, 0, 1, 3, 3, 0, 3], dtype=int32)

f, (ax1, ax2) = plt.subplots(1, 2, sharey=True,figsize=(11,7))
ax1.set_title('K Means cluster')
ax1.scatter(data[0][:,0],data[0][:,1],c=kmeans.labels_,cmap='rainbow')
ax2.set_title("Original/main")
ax2.scatter(data[0][:,0],data[0][:,1],c=data[1],cmap='rainbow')


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k means clustering  examples
k means clustering examples
Summary:
In this section we learn the k means clustering algorithm python example in detailed, about this section if you have any problem then please comment me.
Tags:
k means clustering algorithm python example, python, machine learning
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