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It seems I can't access the specific page directly. However, I can help you with performing hierarchical clustering using dendrograms based on distance matrices. 

For single linkage, complete linkage, and average linkage methods, you need a distance matrix and follow these steps:

1. **Single Linkage (Minimum linkage)**:
   - Merge the pair of clusters with the smallest minimum distance.
   - Update the distance matrix to reflect the distance between the new cluster and all other clusters.
   - Repeat until all points are in one cluster.

2. **Complete Linkage (Maximum linkage)**:
   - Merge the pair of clusters with the smallest maximum distance.
   - Update the distance matrix to reflect the distance between the new cluster and all other clusters.
   - Repeat until all points are in one cluster.

3. **Average Linkage**:
   - Merge the pair of clusters with the smallest average distance.
   - Update the distance matrix to reflect the distance between the new cluster and all other clusters.
   - Repeat until all points are in one cluster.

Could you please provide the distance matrix or more details, so I can help you more specifically?

Would you like more details or have any questions?

Here are some related questions to expand on this topic:
1. What is a distance matrix, and how is it constructed?
2. How does single linkage clustering differ from complete linkage clustering?
3. What are the advantages and disadvantages of using single linkage clustering?
4. How is the dendrogram used to determine the number of clusters?
5. Can you explain how average linkage clustering is calculated?
6. How do outliers affect hierarchical clustering?
7. What are some applications of hierarchical clustering in real-world scenarios?
8. How can hierarchical clustering be implemented in Python?

**Tip**: In hierarchical clustering, the choice of linkage method can significantly affect the resulting dendrogram and clusters.

Learn how to perform hierarchical clustering using dendrograms based on distance matrices. Explore single linkage, complete linkage, and average linkage methods with detailed explanations. Ideal for advanced learners and professionals seeking insights into clustering algorithms.

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