DBSCAN Algorithm

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised machine learning algorithm used for density-based clustering, identifying clusters of arbitrary shapes and handling noise effectively. Current research focuses on improving DBSCAN's efficiency and robustness for high-dimensional data and large datasets, including adaptations for neuromorphic computing and the development of novel algorithms like AMD-DBSCAN and IPD to address limitations in handling multi-density data and large-scale datasets. These advancements are impacting diverse fields, from anomaly detection in enterprise processes and protein structure analysis to applications in environmental science and music analysis, demonstrating DBSCAN's versatility and continued relevance.