Unsupervised Learning #

• Works on unlabelled raw data.
• The algorithm discovers hidden patterns without prior knowledge of outcomes.
• Requires no human intervention during training.
• Does not make direct predictions — it groups or organises data instead.
• Carries a higher risk because there’s no ground truth to verify results.
• Common techniques include Clustering, Association, and Dimensionality Reduction.

stateDiagram-v2

  %% ML maths-based colours (same palette as supervised)
  classDef probability fill:#d1fae5,stroke:#065f46,stroke-width:1px
  classDef geometry fill:#ffedd5,stroke:#9a3412,stroke-width:1px
  classDef category font-style:italic,font-weight:bold,fill:#f3f4f6,stroke:#374151

  %% Root
  USL: Unsupervised Learning

  %% Main branches
  USL --> CLU:::category
  CLU: Clustering

  USL --> DR:::category
  DR: Dimensionality Reduction

  %% Clustering algorithms
  CLU --> KM:::geometry
  KM: K-Means

  CLU --> HC:::geometry
  HC: Hierarchical Clustering

  CLU --> DB:::geometry
  DB: DBSCAN

  %% Probabilistic models
  USL --> PM:::category
  PM: Probabilistic Models

  PM --> GMM:::probability
  GMM: Gaussian Mixture Model

  PM --> HMM:::probability
  HMM: Hidden Markov Model

Clustering #

• Groups similar data points together based on shared features.
• Commonly used for market segmentation, image compression, and anomaly detection.

Common Types of Clustering #

• K-Means Clustering – Divides data into K groups based on similarity.
• Hierarchical Clustering – Builds a hierarchy (tree) of clusters.
• DBSCAN (Density-Based Spatial Clustering) – Groups points close in density; identifies noise/outliers.

Association #

• Identifies relationships or correlations between variables in a dataset.
• Commonly used in market basket analysis (e.g. “Customers who bought X also bought Y”).

Common Techniques #

• Apriori Algorithm – Finds frequent itemsets and generates association rules.
• Eclat Algorithm – Similar to Apriori but uses set intersections for faster computation.

Dimensionality Reduction #

• Reduces the number of input variables to simplify data.
• Helps remove noise and redundancy.
• Commonly used in data pre-processing and visualisation.

Common Techniques #

• Principal Component Analysis (PCA) – Projects data onto fewer dimensions while keeping most variance.
• Linear Discriminant Analysis (LDA) – Focuses on class separation.
• t-SNE (t-Distributed Stochastic Neighbour Embedding) – Used for visualising high-dimensional data.
• Autoencoders – Neural networks that compress and reconstruct data.

mindmap
  root(Unsupervised Learning)
    Clustering
      K Means
      Hierarchical Clustering
      DBSCAN
    Dimensionality Reduction
      PCA
      t SNE
      Autoencoders
    Probabilistic Models
      Gaussian Mixture Model
      Hidden Markov Model

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