As I have used DBSCAN method, I delved deeper about the DBSCAN clusturing method.
DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, is like a detective for finding groups of data points in a map or dataset. Imagine you have a map with many dots on it, and you want to know if there are any meaningful clusters of points. DBSCAN helps with this.
DBSCAN doesn’t just look at how far apart points are; it also considers how many points are close to each other. It’s like saying, “Let’s see if a bunch of dots are huddled together.” If they are, DBSCAN says, “Aha! That’s a cluster.”
In these clusters, there are some special points called “core points.” Core points are like the central hubs of a cluster, with lots of other points around them. They’re like the heart of the cluster.
DBSCAN also looks at how points are connected. If you can travel from one point to another by hopping through a chain of nearby points, DBSCAN says those points are part of the same cluster. It’s like connecting the dots on the map to find a cluster shape.
Not every point is in a cluster, though. Some points are lonely and don’t have many friends nearby. DBSCAN calls them “noisy” or “outliers.” These points don’t belong to any cluster; they’re just on their own.
One of the neat things about DBSCAN is that you don’t have to tell it how many clusters you expect to find. It figures that out by itself based on the data. So, it’s like having an automatic cluster-finding detective for your data analysis.
I will discuss about pros and cons of DBSCAN during my next update