Lecture 57:- Agglomerative code

Here's an example of how you can implement the Agglomerative Hierarchical Clustering algorithm using Python and the scikit-learn library:

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import numpy as np from sklearn.datasets import make_blobs from sklearn.cluster import AgglomerativeClustering import matplotlib.pyplot as plt # Generate synthetic data data, _ = make_blobs(n_samples=300, centers=4, random_state=42) # Create AgglomerativeClustering model n_clusters = 4 model = AgglomerativeClustering(n_clusters=n_clusters) # Fit the model to the data labels = model.fit_predict(data) # Plot the data colored by cluster assignments plt.scatter(data[:, 0], data[:, 1], c=labels, cmap='rainbow') plt.xlabel('Feature 1') plt.ylabel('Feature 2') plt.title('Agglomerative Clustering') plt.show()

This code snippet demonstrates the following steps:

1. Generate synthetic data using make_blobs.
2. Create an AgglomerativeClustering model with the desired number of clusters.
3. Fit the model to the data.
4. Retrieve the cluster assignments and plot the data points colored by cluster assignments.

You can replace the dataset generation step with your own data if you have a different dataset to cluster. Additionally, you can adjust hyperparameters of the AgglomerativeClustering model, such as the linkage type (e.g., 'ward', 'complete', 'average', etc.) and the distance metric used for merging clusters.

Agglomerative Hierarchical Clustering has different linkage strategies that affect how clusters are merged. For example, 'ward' linkage minimizes the variance within clusters, 'complete' linkage considers the maximum pairwise distance between points in different clusters, and 'average' linkage considers the average pairwise distance.

Remember that Agglomerative Hierarchical Clustering is a versatile algorithm that provides insights into the hierarchical structure of your data. It can be particularly useful when your data has a natural hierarchical organization.