Density-Based Clustering: DBSCAN vs. HDBSCAN

Cluster Analysis is a pertinent domain in data science that enables the grouping of similar objects into distinct subgroups. While there are different families of clustering algorithms, the most widely known is K-Means. This is a centroid-based algorithm, meaning that objects in the data are clustered by being assigned to the nearest centroid. However, a major pitfall of K-Means is its lack of detecting outliers, or noisy data points, which leads them to be classified incorrectly. Furthermore, K-Means has an intrinsic preference for globular clusters and does not work very well on data comprised of arbitrarily shaped clusters.