Lecture 54:- K-Means Code

Here's an example of how you can implement the K-Means clustering algorithm using Python and the scikit-learn library:

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import numpy as np from sklearn.datasets import make_blobs from sklearn.cluster import KMeans import matplotlib.pyplot as plt # Generate synthetic data data, _ = make_blobs(n_samples=300, centers=4, random_state=42) # Create K-Means model k = 4 # Number of clusters model = KMeans(n_clusters=k, random_state=42) # Fit the model to the data model.fit(data) # Get cluster assignments and centroids cluster_assignments = model.labels_ centroids = model.cluster_centers_ # Plot the data and centroids plt.scatter(data[:, 0], data[:, 1], c=cluster_assignments, cmap='rainbow') plt.scatter(centroids[:, 0], centroids[:, 1], marker='X', s=200, c='black') plt.xlabel('Feature 1') plt.ylabel('Feature 2') plt.title('K-Means Clustering') plt.show()

This code snippet demonstrates the following steps:

1. Generate synthetic data using make_blobs.
2. Create a K-Means model with the desired number of clusters.
3. Fit the model to the data.
4. Retrieve cluster assignments and centroids.
5. Plot the data points colored by cluster assignments and show the centroids.

You can replace the dataset generation step with your own data if you have a different dataset to cluster. Additionally, you can adjust the number of clusters (k) and other hyperparameters of the K-Means algorithm to suit your specific needs.

Remember that K-Means is a versatile algorithm, but its performance can be sensitive to the initial placement of centroids. Running K-Means multiple times with different initializations can help mitigate this issue.