Unsupervised Learning: Clustering

K Means Clustering

Delve into one of the most widely used unsupervised algorithms—K-Means. This module explains how the algorithm partitions data into k clusters based on centroid proximity, making it ideal for structured numerical data.

Topics Covered:

• The Steps in the K-Means Algorithm
  Understand the K-Means workflow—initializing centroids, assigning data points to the nearest centroid, recalculating centroids, and iterating until convergence. Learn the mathematical intuition and objective functions behind it.
  
  
• Graphically Visualise the Steps of the K-Means Algorithm
  Use visual tools to break down the iterative process of clustering in 2D and 3D space. Visualization helps internalize how data points shift between clusters and how centroids evolve over time.

• Practical Considerations While Using the K-Means Algorithm
  Learn to navigate common pitfalls such as choosing the right k-value, handling initialization bias, and working with non-spherical or overlapping clusters. Understand how to scale features and pre-process datasets for optimal results.

Executing K Means in Python

This hands-on module takes your understanding of K-Means from theory to practice. You'll implement the algorithm on a real-world dataset—Online Retail Data—to discover purchasing patterns and business insights.

Topics Covered:

• Data Preparation
  Learn how to clean, normalize, and transform raw data. Focus on selecting relevant features, removing duplicates, and handling missing values to create a clean dataset ready for clustering.
  
  
• How to Make the Clusters
  Apply the K-Means algorithm step-by-step using Python. Visualize clusters using scatter plots, and analyze how the algorithm partitions the dataset into meaningful groups.
  
  
• Decide the Optimal Number of Clusters
  Master techniques like the Elbow Method, Silhouette Score, and Gap Statistics to objectively determine the ideal number of clusters for your dataset—crucial for model stability and clarity.
  
  
• How to Interpret the Results
  Evaluate clustering outcomes through statistical summaries, cluster profiles, and visualization techniques. Learn how to turn cluster labels into actionable insights for strategic decision-making.

Hierarchical Clustering

Unlike K-Means, Hierarchical Clustering doesn’t require predefined cluster counts. It builds nested clusters and represents data as a tree (dendrogram), offering a flexible, visual approach to segmentation.

Topics Covered:

• Hierarchical Clustering Algorithm
  Understand both Agglomerative (bottom-up) and Divisive (top-down) clustering methods. Learn how distance metrics and linkage criteria (e.g., complete, single, average, Ward's) influence clustering structure.
  
  
• Interpreting the Dendrogram
  Visualize hierarchical relationships through dendrograms. Learn how to read and analyze tree-like diagrams to uncover natural cluster boundaries and relationships between data points.
  
  
• Cutting the Dendrogram
  Identify optimal cut points on the dendrogram to derive the desired number of clusters. Learn how thresholding the distance matrix helps in segmenting the tree into meaningful groups.
  
  
• Types of Linkages
  Explore linkage strategies like single-linkage, complete-linkage, average-linkage, and Ward’s method. Understand how each impacts cluster shape, granularity, and separation.

Other Forms of Clustering

Advance to more specialized clustering techniques for categorical and mixed datasets, and explore density-based methods that handle noise and non-linear structures with high precision.

Topics Covered:

• K-Mode Clustering
  Explore clustering for purely categorical data. K-Mode replaces means with modes and uses Hamming distance, making it ideal for datasets like survey responses or categorical demographics.
  
  
• K-Prototype
  Learn to handle mixed-type datasets (numerical + categorical) using K-Prototype, a hybrid algorithm that brings the best of K-Means and K-Mode together for real-world applications.
  
  
• DBSCAN Clustering
  Understand Density-Based Spatial Clustering of Applications with Noise (DBSCAN)—an algorithm that finds clusters based on point density. It’s ideal for identifying outliers and non-convex clusters in noisy datasets.
  
  
• Gaussian Mixture Model
  Discover how GMM uses probabilistic models to represent subpopulations. Unlike hard clustering, GMM assigns a probability to each point for belonging to a cluster, enabling soft and flexible segmentation.