Introduction to DBSCAN

In DBSCAN, density is evaluated as a combination of neighborhood radius and minimum points found in a neighborhood deemed a cluster. This concept can be driven home if we reconsider the scenario where you are tasked with organizing an unlabeled shipment of wine for your store. In the previous example, it was made clear that we can find similar wines based on their features, such as chemical traits. Knowing this information, we can more easily group together similar wines and efficiently have our products organized for sale in no time. In the real world, however, the products that you order to stock your store will reflect real-world purchase patterns. To promote variety in your inventory, but still have sufficient stock of the most popular wines, there is a highly uneven distribution of product types that you have available. Most people love the classic wines, such as white and red; however, you may still carry more exotic wines for your customers who love expensive...