How do I implement K-means clustering?

How do I implement K-means clustering?

To implement K-means clustering, you first need to choose the number of clusters (K) you want to create.

What are the steps involved in the K-means algorithm?

The main steps are: 1) Initialize K centroids, 2) Assign data points to the nearest centroid, 3) Update centroids, and repeat.

Can you show me a code example in Python?

Sure! Here's a simple example using scikit-learn:

from sklearn.cluster import KMeans
import numpy as np

# Sample data
X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])

# Create KMeans instance
kmeans = KMeans(n_clusters=2)

# Fit the model
kmeans.fit(X)

# Get cluster centers
print(kmeans.cluster_centers_)

What should I consider when choosing the number of clusters?

You can use methods like the Elbow Method or Silhouette Score to help determine the optimal number of clusters.