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Hierarchical Clustering in Machine Learning
A comprehensive guide to hierarchical clustering in machine learning, covering both theoretical concepts and practical implementation in R. The article explains clustering types, linkage methods, opti
mal cluster determination, and dendrogram interpretation with detailed code examples.

Reasons to Read -- Learn:

  • how to implement hierarchical clustering in R using specific packages like cluster and factoextra, with detailed code examples and visualizations
  • differences between five major linkage methods (complete, single, mean, centroid, and Ward's) and understand when to use each for optimal clustering results
  • how to interpret dendrograms correctly, including common misinterpretations to avoid, and techniques for determining the optimal number of clusters using three different methods
    • 8 min readauthor: A.I Hub
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