Implement some of the approaches for automatic choice of the eps parameter.
- AutoEpsDBSCAN: Computes a smoothed k-distance graph to automatically detect the "knee," suitable for large, noisy datasets.
- GA-DBSCAN: Combines Genetic Algorithms (GA) with DBSCAN to automatically find the optimal eps by maximizing cluster accuracy, addressing the difficulty of setting this parameter in varying densities.
- CVR-DBSCAN: Uses curvature analysis on the data to estimate appropriate eps and MinPts parameters.
- KNN-Probability: Uses a probability-based approach (often using Gaussian distributions) to dynamically determine eps for each data frame, particularly for point cloud data.
Implement some of the approaches for automatic choice of the eps parameter.