Statistical Analysis and Modeling in R: Performing Clustering

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Clustering is an unsupervised learning algorithm that self-discovers patterns in data and helps identify logical groupings. Use this course to distinguish between supervised and unsupervised learning and recognize how regression and classification algorithms differ from clustering. Examine the basic principles of clustering models and how k-means clustering finds logical groupings in your data. Learn the evaluation techniques used in clustering and find the optimal number of clusters in your data using both the elbow method and the Silhouette score. Perform clustering on a dataset with multiple attributes and visualize clusters in your data using principal components. When you've completed this course, you'll be able to find groupings in your data using k-means clustering and compute the optimal number of clusters for your data.