DBSCAN : Density-Based Spatial Clustering of Applications with Noise is a density-based clustering algorithm used to discover clusters of data points in a dataset. It’s particularly effective for finding clusters of arbitrary shapes and handling outliers or noise.
- The choice of parameters ε – distance threshold and minPts- minimum points required for a core point is crucial and should be determined based on the dataset and problem domain.
- DBSCAN is efficient for datasets with varying densities but may struggle when clusters have significantly different densities.
- It doesn’t require us to specify the number of clusters beforehand, making it suitable for scenarios where the cluster count is unknown.
- DBSCAN can identify clusters of different shapes and sizes, and it naturally handles noise points.
- The algorithm recursively expands the cluster by examining the ε-neighborhood of the core point’s neighbors. If any of these neighbors are also core points, they are added to the same cluster, and their ε-neighborhoods are explored in turn. This process continues recursively until there are no more core points in the ε-neighborhood
DBSCAN is widely applied in various fields, including image analysis, spatial data, and anomaly detection, where clusters may not be well-defined or uniformly distributed