Unsupervised learning

If you don’t know the categories or labels in your data, you’re dealing with unsupervised learning, where the goal is to find patterns, groupings, or structure within the data without labeled outcomes. Unlike supervised learning (which involves classification or regression), unsupervised learning works with data that doesn’t have predefined labels or targets.

Common Types of Unsupervised Learning

1. Clustering:
   • Objective: Group similar data points together based on inherent patterns.
   • Example Algorithms:
     • K-Means: Groups data into KKK clusters based on the similarity of features.
     • Hierarchical Clustering: Builds a hierarchy of clusters.
     • DBSCAN (Density-Based Clustering): Identifies clusters of varying shapes based on data density.
   • Use Case: Grouping customers into segments based on their purchasing behavior, even when the categories (e.g., high-value or low-value customers) aren’t known.
2. Dimensionality Reduction:
   • Objective: Reduce the number of features or variables while preserving important information.
   • Example Algorithms:
     • Principal Component Analysis (PCA): Finds a new set of variables (principal components) that capture the most variance in the data.
     • t-SNE (t-Distributed Stochastic Neighbor Embedding): Helps visualize high-dimensional data in lower dimensions.
   • Use Case: Simplifying data for visualization or reducing noise while preserving important patterns.
3. Anomaly Detection:
   • Objective: Identify unusual or outlier data points that do not conform to the general pattern.
   • Example Algorithms:
     • Isolation Forest: Detects anomalies by isolating data points that behave differently.
     • Autoencoders (in deep learning): Learn to reconstruct input data and identify points with high reconstruction errors as anomalies.
   • Use Case: Detecting fraudulent transactions in financial data.
4. Association Rule Learning:
   • Objective: Discover relationships or associations between variables in large datasets.
   • Example Algorithms:
     • Apriori: Used to find frequent itemsets and association rules (e.g., “people who buy X also tend to buy Y”).
   • Use Case: Market basket analysis in retail to discover product combinations frequently bought together.

Summary

If you don’t know the categories or labels, you’re likely dealing with unsupervised learning methods such as clustering, dimensionality reduction, anomaly detection, or association rule learning, where the algorithm identifies patterns, groupings, or outliers from the data without any prior knowledge of labels.