BE - Machine Learning

A dendrogram is a tree-like diagram that shows the hierarchical relationships between different items or groups. It displays how data points cluster together step by step, resembling an upside-down tree with branches. 
How it works: 

    Starts with individual data points at the bottom
    Shows which points/groupings merge together at each step
    Height of branches indicates how similar or dissimilar the groups are
    Longer branches mean greater differences between merged groups
     

Key Uses: 
1. Cluster Analysis 

    Identify natural groupings in data
    Determine optimal number of clusters
    Understand relationships between data points
     

2. Biology & Genetics 

    Show evolutionary relationships between species
    Display genetic similarities between organisms
    Classify organisms based on characteristics
     

3. Market Research 

    Group customers with similar behaviors
    Segment markets based on preferences
    Analyze product similarities
     

4. Document Analysis 

    Cluster similar documents or articles
    Organize large text collections
    Find related topics
     

Reading a Dendrogram: 

    Vertical axis: Distance/similarity measure
    Horizontal axis: Individual items or clusters
    Branches: Show when groups merge
    Height of branches: Indicates dissimilarity (higher = more different)
     

Example: 

In customer segmentation, a dendrogram might show how 100 customers gradually group together based on purchasing behavior, revealing natural customer segments. 

Bottom line: A dendrogram is like a family tree for data, showing how similar items cluster together and helping you understand natural groupings in your dataset.