# DBSCAN CLUSTERING ALGORITHM

DBSCAN is an abbreviation for density-based spatial clustering of noise-affected applications. Unlike k-means, it is a density-based clustering technique. This is an effective approach for locating outliners in a data collection. Based on the density of data points in various locations, it finds arbitrarily formed clusters. It divides regions into low-density zones in order to find outliers between high-density clusters. When it comes to coping with irregularly shaped data, our approach outperforms k-means.

DBSCAN defines clusters using two parameters: minPts (the minimal number of data points that must be clustered together for an area to be considered high-density) and eps (the distance used to determine if a data point is in the same area as other data points). It is crucial that the initial parameters are chosen correctly for this technique to succeed.

# THE MAIN CODE OF DBSCAN CLUSTERING ALGORITHM

# Full Code Of Implementing DBSCAN Clustering Algorithm

Practicing DBSCAN clustering algorithm (Download the dataset here) and there is also has Hierarchical Clustering algorithm (check more detailed about Hierarchical Clustering here):

`from sklearn.cluster import DBSCAN`