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