Why DBSCAN?

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) offers a powerful alternative to traditional clustering algorithms like K-means and hierarchical clustering, especially when dealing with clusters of arbitrary shapes and datasets containing noise.

The table above highlights the key advantages of DBSCAN: its ability to find clusters of any shape, its robustness to noise, and its automatic determination of the number of clusters.

Therefore, DBSCAN is particularly well-suited for scenarios where:

Clusters have irregular shapes;
Noise points are present and need to be identified;
The number of clusters is not known beforehand;
Data density varies across the dataset.

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Section 5. Chapter 1

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Course Content

Cluster Analysis

1. Clustering Fundamentals

Introduction to Clustering Clustering Vs Classification Clustering Algorithms and Libraries

2. Core Concepts

Missing Values Handling Categorical Features Encoding Data Normalization Distance Measures Linkages Challenge: Preprocessing the Dataset

3. K-Means

4. Hierarchical Clustering

How Hierarchical Clustering Works?Optimal Number of Clusters Implementing on Dummy Dataset Implementing on Customers Dataset Challenge: Implementing Hierarchical Clustering

5. DBSCAN

Why DBSCAN?How DBSCAN Works?How to Assign Points to the Clusters?Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing DBSCAN

6. GMMs