What is K-means clustering?

K-means clustering is an unsupervised learning algorithm designed to partition data into K distinct clusters based on feature similarity. In this context, similarity is often determined using distance metrics, such as Euclidean distance, which measures how close data points are to each other in a multi-dimensional space.

The primary function of K-means is to identify groups within a dataset without prior knowledge of data labels. During the K-means process, the algorithm initializes K centroids, which represent the center of each cluster. It iteratively assigns data points to the nearest centroid and then recalculates centroids based on the data points assigned to each cluster. This cycle continues until the centroids no longer change significantly, indicating that the algorithm has achieved convergence.

This methodology is particularly useful in exploratory data analysis where the underlying structure of the data is not known. By clustering similar data points, K-means can reveal natural groupings, assisting in the identification of patterns within the data. Overall, its application spans various fields, including market segmentation, image compression, and anomaly detection.

In contrast, supervised learning techniques involve labeled data and focus on making predictions or classifications, which does not apply to K-means. Dimensionality reduction methods, while related to clustering tasks,