Understanding DBSCAN
Density-based spatial clustering of applications with noise (DBSCAN) is an unsupervised learning technique that performs clustering based on the density of the points. The basic idea is based on the assumption that if we group the data points in a crowded or high-density space together, we can achieve meaningful clustering.
This approach to clustering has two important implications:
- Using this idea, the algorithm is likely to cluster together the points that exist together regardless of their shape or pattern. This methodology helps in creating clusters of arbitrary shapes. By “shape,” we refer to the pattern or distribution of data points in a multi-dimensional space. This capability is advantageous because real-world data is often complex and non-linear, and the ability to create clusters of arbitrary shapes enables more accurate representation and understanding of such data.
- Unlike the k-means algorithm, we do not need to...
