Clustering Algorithms

Clustering algorithms are a type of unsupervised machine learning technique used to group similar data points together based on their features. These algorithms identify inherent structures in data without prior labels, making them useful for exploratory data analysis. Common characteristics include the ability to handle large datasets and the application of distance metrics to determine similarity. Popular clustering methods include K-means, hierarchical clustering, and DBSCAN, each with its unique approach to grouping data. Use cases range from customer segmentation in marketing to image analysis and anomaly detection in various fields.