K-Means Clustering: Discussion with Clinical Dataset and Application

This discussion post provides an overview of the K-means clustering technique, a popular non-hierarchical clustering method. It highlights the benefits of K-means, such as its suitability for solving M-clustering problems, its speed compared to hierarchical methods for large datasets with small k values, and the tighter, changeable clusters it produces. The post then illustrates the technique using a clinical dataset of four medicines with two features: weight index (X) and pH (Y). The example demonstrates the steps of K-means clustering, including initial centroid selection, distance calculation (using Euclidean distance), object clustering based on minimum distance, and iterative centroid determination until the grouping stabilizes. The discussion also touches on the difference between supervised and unsupervised learning, classifying K-means as unsupervised due to its use of unlabeled data. The post concludes by referencing external resources for further understanding of K-means clustering.