K-Means clustering is an unsupervised machine learning algorithm widely used in Data Science and Big Data analytics to group similar data points into clusters based on their features. It partitions a dataset into K distinct, non-overlapping clusters by minimizing the within-cluster sum of squares (WCSS), which measures the variance within each cluster. The algorithm works iteratively by initializing K centroids, assigning each data point to the nearest centroid using Euclidean distance, recalculating centroids as the mean of assigned points, and repeating the process until convergence. To apply K-Means clustering in R, a sample dataset such as the built-in “iris” dataset can be used. The dataset contains four numerical variables: Sepal.Length, Sepal.Width, Petal.Length, and Petal.Width. Since K-Means works with numeric data, these features are suitable for clustering. The following R code demonstrates the implementation: ```R # Load dataset data(iris) # Select only numeric features iris_data <- iris[, 1:4] # Set seed for reproducibility set.seed(123) # Apply K-Means with 3 clusters kmeans_result <- kmeans(iris_data, centers = 3, nstart = 25) # View cluster assignments kmeans_result$cluster # Visualize clusters using first two features plot(iris_data[,1:2], col = kmeans_result$cluster, pch = 19, xlab = "Sepal Length", ylab = "Sepal Width", main = "K-Means Clustering Results") # Add cluster centroids points(kmeans_result$centers[,1:2], col = 1:3, pch = 8, cex = 2) ``` In this