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Join Andrey Holz, Ph. D., for this comprehensive lecture on Clustering Algorithms, designed to provide an in-depth understanding of key techniques and practical applications.
? What’s included:
? K-Means: Compact clustering and WCSS evaluation.
⚡ MiniBatch K-Means: Speed and scalability for large datasets.
? DBSCAN: Noise-resistant density-based clustering.
? Fuzzy C-Means: Overlapping clusters with flexible membership.
? Hierarchical Clustering: Tree-like nested structures.
? Metrics Analysis: Internal (Silhouette, Calinski-Harabasz, Davies-Bouldin) and External (ARI, NMI) metrics explained.
?️ Visualization: PCA-based cluster plots for clear comparisons.
⏱️ Hands-On Comparisons: Explore runtime, accuracy, and practical use cases.
✏️ **Timestamps:**
- 0:00:00 Introduction and Agenda
- 0:01:00 Types of ML: Supervised, Unsupervised, Semi-supervised
- 0:02:00 Introduction to Clustering: What is Clustering?
- 0:03:06 How do we separate data for clustering? Similarity, Distance, Density. Distance Measures
- 0:05:08 Types of Clustering Algorithms
- 0:08:22 Hard Clustering vs Soft Clustering
- 0:10:05 Overview of Clustering Algorithms
- 0:10:08 K-Means Clustering: Overview
- 0:11:40 K-Means Clustering: Algorithm Steps
- 0:14:18 K-Means Clustering: Strengths and Limitations
- 0:17:13 Hierarchical Clustering: Overview
- 0:18:18 Hierarchical Clustering: Algorithm Steps
- 0:20:27 Hierarchical Clustering: Strengths and Limitations
- 0:23:37 DBSCAN: Overview
- 0:25:02 DBSCAN: Algorithm Steps
- 0:27:16 DBSCAN: Strengths and Limitations
- 0:29:40 Fuzzy C-Means Clustering: Overview
- 0:30:47 Fuzzy C-Means Clustering: Algorithm Steps
- 0:33:20 Fuzzy C-Means Clustering: Strengths and Limitations
- 0:35:31 Comparative Analysis of different clustering algorithms
- 0:38:20 Evaluating Clustering Results: Internal Metrics
- 0:39:37 Evaluating Clustering Results: External Metrics
- 0:42:38 Practical Considerations in Clustering
- 0:42:43 Choosing the Number of Clusters (k)
- 0:44:44 Handling High-Dimensional Data
- 0:45:16 Feature Scaling and Normalization
- 0:45:39 Dealing with Outliers and Noise
- 0:45:46 Computational Efficiency and Scalability
- 0:46:38 Initialization Sensitivity
- 0:47:24 Live Coding & Demos
- 0:49:26 Advanced Topics and Best Practices
- 0:49:35 Advanced Clustering Techniques
- 0:53:04 Best Practices Summary
- 0:55:06 Key Takeaways
- 0:56:33 Thank You + some motivation
? Key Takeaways:
Deep dive into clustering algorithms with practical insights.
Comparative understanding of algorithm strengths and weaknesses.
Evaluation using both internal and external metrics.
Practical tips for choosing the right clustering technique for your data.
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