DBSCAN
Explore the DBSCAN clustering algorithm to identify high-density regions as clusters and low-density regions as noise. Understand core samples, neighbors, and how to configure parameters like epsilon and minimum samples in scikit-learn for scalable clustering without assuming cluster shapes.
We'll cover the following...
We'll cover the following...
Chapter Goals:
- Learn about the DBSCAN algorithm
A. Clustering by density
The mean shift clustering algorithm in the previous chapter usually performs sufficiently well and can choose a reasonable number of clusters. However, it is not very scalable due to computation time and still makes the assumption that clusters have a "blob"-like shape (although this assumption is not as strong as the one made by K-means).
Another clustering algorithm that also automatically chooses the number of clusters is ...