In this video, we are going to see the DBSCAN clustering algorithm in Machine Learning. 

DBSCAN stands for Density-Based spatial clustering of Application.

It is an unsupervised learning technique used in machine learning algorithms and model building. It is a clustering technique used to distinguish between high- and low-density clusters.This algorithm is based on the intuitive notion of "clusters" and "Noise". 

Keynotes:
1) Clustering Algorithm
2) Noisy Points
3) Most Powerful clustering ago

DBSCAN algorithm requires two parameters:

1) eps : It used to define the neighborhood around a data point i.e. if the distance between two points is lower or equal to ‘eps’ then they are considered neighbors. If the eps value is chosen too small then large part of the data will be considered as outliers. If it is chosen very large then the clusters will merge and the majority of the data points will be in the same clusters. One way to find the eps value is based on the k-distance graph.

2) MinPts: Minimum number of neighbors (data points) within eps radius. Larger the dataset, the larger value of MinPts must be chosen. As a general rule, the minimum MinPts can be derived from the number of dimensions D in the dataset as, MinPts >= D+1. The minimum value of MinPts must be chosen at least 3

DBSCAN Clustering in ML | Density based clustering
https://www.geeksforgeeks.org/dbscan-clustering-in-ml-density-based-clustering/