What type of algorithm is K-Means?

K-Means is categorized as an unsupervised clustering algorithm, which is designed to group similar data points into distinct clusters based on their characteristics, without requiring labeled data. The goal of K-Means is to partition the dataset into K predefined clusters, where each data point is assigned to the cluster with the nearest mean (centroid).

In the context of unsupervised learning, K-Means does not depend on the training data being labeled, as it seeks to find inherent patterns or groupings in the data itself. This differs significantly from supervised learning algorithms, which rely on labeled input-output pairs to make predictions.

The effectiveness of K-Means comes from its simplicity and efficiency, making it widely used in various applications such as customer segmentation, image compression, and as a preprocessing step in more complex algorithms. Understanding K-Means as an unsupervised clustering algorithm is crucial for applying it effectively in data science projects.