Mastering Clustering in Machine Learning with R

Build an intermediate understanding of unsupervised learning by applying common clustering techniques in R. This path covers algorithm parameter tuning, cluster formation, and the interpretation of clustering results.

Explore K-means clustering, hierarchical clustering with dendrograms, and density-based clustering with DBSCAN. Learn how each method identifies structure in data, how to select appropriate distance metrics, and when to use different approaches.

Assess and refine clustering models using the Silhouette Score, Davies-Bouldin Index, and cross-tabulation analysis. This path is designed for learners with foundational knowledge of R and machine learning who want practical experience analyzing unlabeled data.

Syllabus

Apply K-means clustering, interpret cluster centroids, and determine optimal cluster numbers
Construct, interpret, and analyze hierarchical clustering dendrograms
Select appropriate distance metrics and linkage criteria for accurate clustering
Use DBSCAN to identify density-based clusters, core points, and noise
Evaluate cluster quality using the Silhouette Score and Davies-Bouldin Index
Compare and validate clustering results using cross-tabulation analysis
Visualize clustering outcomes and performance metrics using ggplot2