28. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Explained
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- Опубликовано: 16 дек 2024
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Explained | Machine Learning Tutorial
In this video, we dive deep into DBSCAN, a popular clustering algorithm in machine learning. Learn how DBSCAN groups data points based on their density, making it an excellent choice for datasets with noise or outliers. We'll explore:
What DBSCAN is and how it works
Key parameters: epsilon (eps) and min_samples
How DBSCAN can identify clusters of varying shapes and sizes
The concept of "core points," "border points," and "noise points"
A step-by-step example of applying DBSCAN using Python and scikit-learn
Whether you're a beginner or intermediate machine learning enthusiast, this tutorial will give you a solid understanding of DBSCAN and how to implement it for real-world clustering problems.
💻 Code & Resources: Check out the link in the description for access to the code and further reading materials.
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