Machine Learning Clustering
Machine Learning Clustering is an unsupervised learning technique that groups similar data points together into clusters based on their features, without using predefined labels. It identifies inherent patterns or structures in data, such as customer segmentation or image organization, by measuring similarity or distance between data points. Common algorithms include K-Means, DBSCAN, and hierarchical clustering, each suited to different data types and cluster shapes.
Developers should learn clustering when dealing with unlabeled data to discover hidden patterns, such as in market segmentation for targeted marketing, anomaly detection in cybersecurity, or organizing large datasets like images or documents. It's essential for exploratory data analysis, dimensionality reduction, and preprocessing in machine learning pipelines, helping to inform decision-making or improve model performance by grouping similar instances.