Explore unsupervised learning in R through Clustering. Learn data preprocessing, apply algorithms like K-means, DBSCAN, and Hierarchical Clustering, and master validation techniques to assess model performance effectively.
Unlock the secrets of K-means clustering, the backbone of unsupervised learning. You will group data into clusters, identify cluster centroids, and refine cluster quality.
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18 practices
Unpack the complexity of hierarchical clustering, learning to construct and interpret dendrograms for valuable data insights, and apply your knowledge to real-world data.
Explore the nuanced world of density-based clustering. Learn to navigate through DBSCAN, focusing on connectivity and density functions to identify unique cluster shapes.
Explore an in-depth analysis of clustering model validation, delving into techniques that evaluate, refine, and optimize the performance of clustering algorithms. We'll discuss the Silhouette Score, Davies-Bouldin Index, and Cross-Tabulation Analysis, learning how to implement these practices to identify optimal clustering structures.
