Clustering

Clustering is a machine learning technique used to group similar data points together based on their shared characteristics. It’s an example of unsupervised learning because it finds patterns in data without using labels. In simple terms, clustering helps organize messy data into meaningful groups, much like sorting clothes by type in a closet.

One of the most common clustering methods is k-means clustering, which works by dividing data into a specific number of clusters, called k. The process starts by choosing the number of clusters and randomly selecting k points as centroids, or centers of each group. Each data point is then assigned to the cluster with the nearest centroid, and the centroids are updated based on the average position of the points in their group. This process repeats until the centroids stop moving significantly, meaning the clusters have stabilized. For example, in a business setting, k-means can group customers into categories like budget shoppers, average spenders, and big spenders. Overall, clustering helps transform complex, unlabeled data into clear and actionable insights.