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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