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3. Neighborhood Approaches and DBSCAN

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.

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