Unsupervised Learning
Unsupervised learning deals with unlabeled data where the algorithm must find hidden structures on its own. It’s like sorting a thousand puzzle pieces with no reference image.
Example: In a library, you might group books by topic without reading titles. Machines do the same using clustering methods like k-means or dimensionality reduction techniques like PCA. Example: Customer segmentation groups shoppers by purchasing behavior without predefined categories.
Clustering algorithms group similar data points without needing labeled examples. They discover natural groupings by measuring similarities between observations.
Example: Imagine arranging library books by similarities rather than pre-assigned categories. Approaches include K-means (dividing data into K clusters), hierarchical clustering (nested groupings), and DBSCAN (density-based clusters for irregular shapes). Applications span customer segmentation, document categorization, image compression, and anomaly detection.
Dimensionality reduction transforms high-dimensional data into lower dimensions while preserving essential information. This makes data more manageable for visualization and analysis.
Common approaches include Principal Component Analysis (PCA), which finds principal components that capture data variance, Autoencoders that compress data with neural networks, and t-SNE which preserves local relationships for visualization. These techniques help reduce noise and overfitting while highlighting key patterns.