K-Means Clustering Algorithm

Unsupervised Machine Learning involves using unlabeled data, allowing algorithms to organize data based on patterns and similarities. K-Means Clustering is a popular unsupervised technique that groups data points into K clusters based on their proximity to cluster centroids. This algorithm iterates, reassigning data points to the nearest centroid, and adjusting centroids until convergence. K-Means works best when data is well-separated but may struggle with overlapping data. While fast and efficient, it can be sensitive to noise and initial centroid placement, affecting cluster quality.

For more details, check out the full article: K means Clustering – Introduction.