The following example demonstrates how to run the k-means clustering algorithm in R.library(ggplot2) # Prepare Data data = mtcars # We need to scale the data to have zero mean and unit variance data <- scale(data) # Determine number of clusters wss <- (nrow(data)-1)*sum(apply(data...
Here are the advantages of using K-medoids clustering −Robust to outliers and noise − K-medoids clustering is more robust to outliers and noise than K-means clustering because it uses a representative data point, called a medoid, to represent the center of the cluster. Can handle non-...