Density Based Spatial Clustering of Applications with Noise (DBSCAN)

DBSCAN is a different type of clustering algorithm with some unique advantages. As the name indicates, this method focuses more on the proximity and density of observations to form clusters. This is very different from KMeans, where an observation becomes a part of cluster represented by nearest centroid. DBSCAN clustering can identify outliers, observations which won't belong to any cluster. Since DBSCAN clustering identifies the number of clusters as well, it is very useful with unsupervised learning of the data when we don't know how many clusters could be there in the data.