Hierarchical Mean Clustering Intuition

Overview

• Discusses the mathematics behind hierarchical clustering
• Comparison with k-means clustering: both are unsupervised machine learning techniques, but use different methodologies
• Hierarchical clustering involves building a dendrogram

Hierarchical Clustering Process

• Initial Step: Start with each data point as an individual cluster
• Finding Nearest Points:
  • Determine two nearest points or clusters at each step
  • Use a dendrogram to visualize the hierarchy:
    • X-axis: points
    • Y-axis: distance
• Steps to Build Dendrogram:
  1. Combine nearest points (e.g., P1 and P2)
  2. Determine the next nearest points or clusters (e.g., P3 and P4)
  3. Continue the process until all points are clustered

Distance Calculation

• Uses Euclidean distance:
  • Formula: ( \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2} )

Determining Number of Clusters

• Use the longest vertical line in the dendrogram that does not intersect any horizontal line
• Count the number of points it passes through to determine clusters

Practical Implementation

• Use sklearn to implement hierarchical clustering based on Euclidean distance

Key Concepts

• Unsupervised Learning: Focus on grouping data points based on similarity (distance)
• Dendrogram: A visual representation of the clustering hierarchy

Conclusion

• Hierarchical clustering is a method to group data points based on Euclidean distance.
• The longest vertical line hack helps determine the number of clusters.

Additional Notes

• Ensure understanding of previous topics like k-means and Euclidean distance for better comprehension.
• Consider using sklearn for practical hierarchical clustering applications.

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