SYildiz

1/10/2019 - 7:08 AM

Setting up K-Means Now that we have our random data, let's set up our K-Means Clustering.

The KMeans class has many parameters that can be used, but we will be using these three:

init: Initialization method of the centroids. Value will be: "k-means" k-means: Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. n_clusters: The number of clusters to form as well as the number of centroids to generate. Value will be: 4 (since we have 4 centers) n_init: Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. Value will be: 12

```
import random
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs
%matplotlib inline
#Initialize KMeans with these parameters, where the output parameter is called k_means.
k_means = KMeans(init = "k-means++", n_clusters = 4, n_init = 12)
#Now let's fit the KMeans model with the feature matrix we created above
k_means.fit(X)
#Now let's grab the labels for each point in the model using KMeans' .labels_ attribute and save it as k_means_labels
k_means_labels = k_means.labels_
k_means_labels
#We will also get the coordinates of the cluster centers using KMeans' .cluster_centers_ and save it as k_means_cluster_centers
k_means_cluster_centers = k_means.cluster_centers_
k_means_cluster_centers
#So now that we have the random data generated and the KMeans model initialized, let's plot them and see what it looks like!
# Initialize the plot with the specified dimensions.
fig = plt.figure(figsize=(6, 4))
# Colors uses a color map, which will produce an array of colors based on
# the number of labels there are. We use set(k_means_labels) to get the
# unique labels.
colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))
# Create a plot
ax = fig.add_subplot(1, 1, 1)
# For loop that plots the data points and centroids.
# k will range from 0-3, which will match the possible clusters that each
# data point is in.
for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors):
# Create a list of all data points, where the data poitns that are
# in the cluster (ex. cluster 0) are labeled as true, else they are
# labeled as false.
my_members = (k_means_labels == k)
# Define the centroid, or cluster center.
cluster_center = k_means_cluster_centers[k]
# Plots the datapoints with color col.
ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')
# Plots the centroids with specified color, but with a darker outline
ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6)
# Title of the plot
ax.set_title('KMeans')
# Remove x-axis ticks
ax.set_xticks(())
# Remove y-axis ticks
ax.set_yticks(())
# Show the plot
plt.show()
```