Density-based Clustering
Density-based clustering is a data clustering technique that groups together data points that are closely packed together in high-density regions. It is based on the idea that clusters are dense regions of data separated by regions of lower density. Density-based clustering algorithms identify clusters by finding areas of the data set where the density of data points is higher than a certain threshold. The most popular density-based clustering algorithm is DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which groups together data points that are close to each other and separates them from points that are far away. Density-based clustering is particularly useful for data sets with irregular shapes and noise, as it can identify clusters of any shape and size, and can handle outliers effectively.
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