Unsupervised learning is a type of machine learning where the model is trained on data without explicit labels, focusing instead on uncovering hidden structures and patterns. Clustering is a common technique in unsupervised learning that groups similar data points together, helping to identify natural clusters within the data. For example, clustering can segment customers into distinct groups based on purchasing behavior, allowing businesses to tailor their marketing strategies. Dimensionality reduction, on the other hand, aims to reduce the number of features in the dataset while preserving its essential structure and information. Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are used to simplify complex data, making it easier to visualize and analyze. Both clustering and dimensionality reduction are valuable for understanding the underlying structure of the data, improving model performance, and facilitating more efficient data processing.