Awesome, you have implemented K-Means clustering from scratch!
Writing an algorithm whenever you need it can be very time-consuming and you might make mistakes and typos along the way. We will now show you how to implement K-Means more efficiently – using the scikit-learn library.
Instead of implementing K-Means from scratch, the
sklearn.cluster module has many methods that can do this for you.
from sklearn.cluster import KMeans
For Step 1, use the
KMeans() method to build a model that finds
k clusters. To specify the number of clusters (
k), use the
n_clusters keyword argument:
model = KMeans(n_clusters = k)
For Steps 2 and 3, use the
.fit() method to compute K-Means clustering:
After K-Means, we can now predict the closest cluster each sample in X belongs to. Use the
.predict() method to compute cluster centers and predict cluster index for each sample:
samples = iris.data, use
KMeans() to create an instance called
model to find 3 clusters.
To specify the number of clusters, use the
n_clusters keyword argument.
Next, use the
.fit() method of
model to fit the model to the array of points
After you have the “fitted” model, determine the cluster labels of
Then, print the