Advanced Modeling DBSCAN and Agglomerative Clustering
About
Understand and use DBSCAN and agglomerative clustering techniques.
Learning Objectives
You will be able to:
- Describe the effect of epsilon and min_points on DBSCAN.
- Identify advantages and disadvantages of DBSCAN.
- Implement DBSCAN using
scikit-learn. - Describe the agglomerative clustering technique.
- Identify advantages and disadvantages of agglomerative clustering.
- Implement agglomerative clustering using
scikit-learn.
Content
| Topic | Skills |
|---|---|
| Slides | - Conceptual overview of boosting |
| Clustering with DBSCAN | - Walkthrough of DBSCAN in scikit-learn |
Prerequisites
- Define basic principles of supervised and unsupervised learning.
- Understanding of K-Means clustering technique.
- Prepare features and create models using
scikit-learn. - Plot data.
References
📖 Reference Materials
Important note
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