What Is K-Means Clustering?

K-means clustering is a machine learning algorithm used for unsupervised clustering of data into groups based on similarity. It is a type of partitioning clustering in which the data is divided into k clusters, where k is a pre-defined number of clusters.

The k-means algorithm works by selecting k initial centroids (representative points) for the clusters and then assigning each data point to the nearest centroid based on some distance metric, typically Euclidean distance. After the initial assignment, the algorithm recomputes the centroids based on the current cluster assignments, and then reassigns the data points to the new nearest centroid. This process continues iteratively until convergence, which is achieved when the cluster assignments no longer change.

K-means clustering is commonly used for data segmentation, image segmentation, and customer segmentation. It is also used for anomaly detection and data compression. However, it is important to note that the effectiveness of k-means clustering depends on the choice of the initial centroids and the number of clusters k, which can be determined through techniques such as the elbow method or silhouette score.

Here is an example of k-means clustering in Python using the scikit-learn library:

from sklearn.cluster import KMeans
import numpy as np

# Generate some random data
X = np.random.rand(100, 2)

# Initialize the KMeans object
kmeans = KMeans(n_clusters=3)

# Fit the data to the KMeans object
kmeans.fit(X)

# Get the cluster labels and centroids
labels = kmeans.labels_
centroids = kmeans.cluster_centers_

In this example, we generate a 2-dimensional random dataset and use the KMeans object from scikit-learn to cluster the data into 3 clusters. We then get the cluster labels and centroids for further analysis.