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Clustering Analysis

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  • Introduction and Partitioning Clustering
    • This week provides an introduction to unsupervised learning and clustering analysis. You will delve into partitioning clustering methods, such as K-Means and K-Medoids, understanding their principles and applications.
  • Hierarchical Clustering
    • This week you will explore hierarchical clustering, a method that creates a tree-like structure to represent data similarities.
  • Density-based Clustering
    • This week focuses on density-based clustering, which groups data points based on their density within the dataset.
  • Grid-based Clustering
    • Throughout this week, you will explore grid-based clustering, an approach that partitions the data space into grids for efficient clustering.
  • Dimension Reduction Methods
    • This week introduces dimension reduction techniques as a critical preprocessing step for handling high-dimensional data.
  • Case Study
    • The final week focuses on a comprehensive case study where you will apply clustering and dimension reduction techniques to solve a real-world problem.