Both hierarchical clustering and partitioning methods (K-means and PAM clustering) are suitable to find spherical-shaped clusters or convex clusters and they use a heuristic approach to construct clusters, but they do not rely on a formal model and are severely affected by the presence of noise and outliers in the data. In this recipe, we will look at two advance clustering techniques to handle these shortcomings--density-based clustering and model-based clustering.
Performing Advance clustering
Density-based spatial clustering of applications with noise
Density-based spatial clustering of applications with noise (DBSCAN) is about classifying the data points in the dataset as core data points, border data points, and noise...