Partitioning clustering methods, such as k-means, and hierarchical clustering methods, such as agglomerative clustering, are good for discovering spherical or convex clusters. These algorithms are more sensitive to noise or outliers and work for well-separated clusters:
Intuitively, we can say that a density-based clustering approach is most similar t how we as humans might instinctively group items. In all the preceding figures, we can quickly see the number of different groups or clusters due to the density of the items.
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is based on the idea of groups and noise. The main idea behind it is that each data item of a group or cluster has a minimum number of data items in a given radius.
The main goal of DBSCAN is to discover the dense region that can be computed using minimum number of objects (minPoints) and given radius (eps). DBSCAN has the capability to generate random shapes of clusters and deal...