How does k-means clustering work?

K-means clustering is a popular clustering algorithm used in data analysis and machine learning for grouping data points into clusters based on similarity. It is an unsupervised learning technique that aims to find the optimal cluster centers that minimize the distance between data points and the center of their respective clusters.

K-means clustering process:

The algorithm starts by initializing k cluster centers randomly in the feature space. Then, it iteratively assigns each data point to the nearest cluster center based on a similarity metric (usually Euclidean distance) and recalculates the cluster centers as the means of the data points in each cluster. This process continues until convergence, where the cluster assignments and centers no longer change significantly.

Benefits:

K-means clustering is efficient for large datasets and is easy to implement. It can handle different types of data and is particularly useful for data grouping and data visualization. The algorithm is widely used in various industries for customer segmentation, image processing, anomaly detection, and more.