DBSCAN Clustering Algorithm
Clustering DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Epsilon Neighborhood, Core Points, Border Points, Reachability Distance, Connected Components, Unsupervised Learning, Geometric Algorithms, Computational Geometry, Data Preprocessing, Pattern Recognition, Clustering Validation, Density Estimation, Spatial Indexing, Dimensionality Reduction, Noise Filtering.
Implement a function that performs DBSCAN clustering on a 2D dataset. Given a list of points (tuples), an epsilon radius, and a minimum samples threshold, return a list of cluster labels where noise is represented by 1.