Top Three Clustering Algorithms You Should Know Instead of K-means Clustering

DBSCAN is a clustering algorithm that groups data points into clusters based on the density of the points. The algorithm works by identifying points that are in high-density regions of the data and expanding those clusters to include all points that are nearby. Points that are not in high-density regions and are not close to any other points are considered noise and are not included in any clusters. This means that DBSCAN can automatically identify the number of clusters in a dataset, unlike other clustering algorithms that require the number of clusters to be specified in advance. DBSCAN is useful for data that has a lot of noise or for data that doesn't have well-defined clusters.