July 2020
Intermediate to advanced
550 pages
9h 58m
English
Overview
In this chapter, we will see how neighborhood approaches to clustering work from start to end and implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm from scratch by using packages. We will also identify the most suitable algorithm to solve your problem from k-means, hierarchical clustering, and DBSCAN. By the end of this chapter, we will see how the DBSCAN clustering approach will serve us best in the sphere of highly complex data.
In previous chapters, we evaluated a number of different approaches to data clustering, including k-means and hierarchical clustering. While k-means is the simplest form of clustering, it is still extremely ...