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https://github.com/toatoes/hierarchical-clustering

This is a Hierarchical Clustering with a constant "threshold" that indicate the maximal distance between two clusters to group them. The algorithm stops when no cluster can be merged.
https://github.com/toatoes/hierarchical-clustering

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This is a Hierarchical Clustering with a constant "threshold" that indicate the maximal distance between two clusters to group them. The algorithm stops when no cluster can be merged.

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# Hierarchical-Clustering

This is an implementation of generic Hierarchical Clustering Algorithm as described

in this webpage:
```
http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/hierarchical.html
```

This is a Hierarchical Clustering with a constant "threshold" that indicate

the maximal distance between two clusters to group them. The algorithm stops

when no cluster can be merged.

The distance between two clusters is calculated as the distance between the

medians of the two clusters.


refer to:
```
http://mirlab.org/jang/books/dcpr/dcHierClustering.asp?title=3-2%20Hierarchical%20Clustering%20(%B6%A5%BCh%A6%A1%A4%C0%B8s%AAk)
```


Data Exploration: Understanding the dataset by clustering similar images can help in analyzing diversity and common features.

Data Selection: If you have a large dataset, you can use clustering to select representative images from each cluster for training, thus reducing the size of the dataset while maintaining diversity.

Feature Extraction: You can cluster images based on feature representations from a pre-trained model (like a convolutional neural network), which can help identify similarities among images.