Clustering Algorithms in Machine Learning

Clustering is an unsupervised learning technique that groups similar data points without predefined labels. It helps discover hidden patterns, segment data, and reduce dimensionality in datasets. Key Concepts Clustering: Grouping data points based on similarity or distance metrics. Unsupervised Learning: No labeled data; the model identifies structure independently. Distance Metrics: Commonly used metrics include Euclidean, Manhattan, and Cosine similarity. Popular Clustering Algorithms 1. K-Means Clustering Divides data into K clusters by minimizing the variance within each cluster. Fast, easy to implement, and works well with large datasets. It requires predefining K and is sensitive to outliers. Customer segmentation, image compression. [...]