Density-Based Clustering
A clustering technique that identifies groups of closely packed data points while separating them from sparse regions or noise.
What is Density-Based Clustering?
Density-based clustering identifies regions where data points are closely packed together and separates them from areas containing fewer points. Unlike methods such as K-means, it does not require clusters to have a predefined shape and can discover irregularly shaped groups. DBSCAN is one of the most widely used density-based clustering algorithms.
Why is Density-Based Clustering Important?
Real-world datasets often contain clusters with complex shapes as well as outliers. Density-based methods can identify these structures without requiring the number of clusters to be specified beforehand. This makes them useful for discovering patterns in noisy or irregular datasets.
Common use cases
Density-based clustering is commonly used in anomaly detection, geospatial analysis, customer segmentation, image analysis, pattern recognition, and exploratory data analysis.