Example Concept: Clustering algorithms like K-Means or Hierarchical Clustering can be used to segment customers based on purchasing behavior, website interactions, or demographic data. By grouping customers into clusters, businesses can identify distinct customer segments, tailor marketing strategies, and enhance customer experiences. Dimensionality reduction techniques, such as PCA (Principal Component Analysis), can further simplify complex datasets, making it easier to visualize and interpret customer behavior patterns.