K Means clustering Explained

K Means clustering is a popular machine learning algorithm used for clustering data points into K clusters. The algorithm works by iteratively assigning data points to the nearest cluster centroid and then updating the centroids based on the new assignments. This process is repeated until the centroids no longer change, indicating that the algorithm has converged.

Python Code for K Means Clustering

Below is an example Python code snippet for implementing K Means clustering using the scikit-learn library:

from sklearn.cluster import KMeans

# Generate sample data
X = [[1, 2], [5, 8], [1.5, 1.8], [8, 8], [1, 0.6], [9, 11]]

# Initialize KMeans with 2 clusters
kmeans = KMeans(n_clusters=2)

# Fit the data
kmeans.fit(X)

# Get the cluster labels
labels = kmeans.labels_

# Get the cluster centroids
centroids = kmeans.cluster_centers_

print("Cluster labels:", labels)
print("Cluster centroids:", centroids)
		

In the code snippet above, we first create sample data points in the form of a list of lists. We then initialize the KMeans algorithm with 2 clusters and fit the data to find the cluster labels and centroids. Finally, we print out the cluster labels and centroids.

Conclusion

K Means clustering is a powerful algorithm for clustering data points into K clusters. It is widely used in various applications such as customer segmentation, image compression, and anomaly detection. By understanding how the algorithm works and implementing it in Python, you can leverage its capabilities to analyze and group your data effectively.