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An Introductory Guide to Data Science and Machine Learning

DBSCAN Clustering and Customer Segmentation

Explore DBSCAN clustering, a density-based method that identifies clusters by grouping densely connected points and excluding noise. Understand how to apply DBSCAN for customer segmentation, select key parameters like eps and min_samples, and review its advantages and limitations in handling clusters with varying densities.

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DBSCAN clustering

DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It is based on the idea that clusters are regions of high density separated by regions of low density. Because it treats clusters as areas of high density separated by low-density regions, it can handle clusters of any shape, unlike K-means clustering, which assumes spherical clusters with equal density and no outliers.

It marks points as outliers or noise that lie alone in low-density regions (whose nearest neighbors are too far away). It also makes the assumption that there is noise in the dataset. Clusters in density-based clustering satisfy the following properties:

  1. All points in a cluster are mutually densely connected.

  2. If a point is density reachable from some point of the cluster, it is also a part of the cluster.

Working of DBSCAN clustering

DBSCAN works in the following way.

  • It starts by identifying core samples or points in the dataset. A core sample or point is the one that has at least min_samples or MinPts points around it within a distance of eps ϵ\epsilon ...