sklearn.cluster.DBSCAN¶

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that groups together points that are close to each other and separates points that are far away from each other. It is a density-based algorithm that can identify arbitrarily shaped clusters and handle noise efficiently.

The algorithm takes two parameters as input: epsilon (ε) and the minimum number of points required to form a dense region (min_samples). It starts by selecting a random point and finding all the neighboring points within a distance of ε. If the number of points within the distance ε is greater than or equal to min_samples, then a new cluster is formed. If not, the point is labeled as noise.

Next, the algorithm examines the neighbors of each point in the cluster and adds them to the cluster if they also have enough neighbors within a distance ε. This process continues until all points have been assigned to a cluster or labeled as noise.

DBSCAN has several advantages over other clustering algorithms such as K-means and Hierarchical clustering. It does not require prior knowledge of the number of clusters, it can handle noise effectively, and it can identify clusters of arbitrary shapes. However, it can be sensitive to the choice of parameters ε and min_samples, and it may not work well with data that has varying densities.

In scikit-learn, the DBSCAN algorithm is implemented in the sklearn.cluster.DBSCAN class. It can be used to cluster data in a variety of applications such as image segmentation, anomaly detection, and customer segmentation.