Lecture 56:- Agglomerative 2 - Dendogram

Here's an example of how to create and visualize a dendrogram using the Agglomerative Hierarchical Clustering algorithm and the scipy library in Python:

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import numpy as np from scipy.cluster.hierarchy import dendrogram, linkage import matplotlib.pyplot as plt # Generate synthetic data np.random.seed(0) data = np.random.rand(10, 2) # 10 samples, 2 features # Calculate linkage matrix using Ward linkage linked = linkage(data, method='ward') # Ward linkage minimizes the variance within clusters # Plot the dendrogram plt.figure(figsize=(10, 6)) dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True) plt.xlabel('Data Points') plt.ylabel('Distance') plt.title('Dendrogram') plt.show()

In this code, we generate synthetic data with 10 samples and 2 features. We then calculate the linkage matrix using the 'ward' linkage method, which minimizes the variance within clusters. Finally, we create and display the dendrogram using the dendrogram function from the scipy.cluster.hierarchy module.

The dendrogram visually represents the merging of clusters during the agglomerative process. Each leaf node represents a data point, and the vertical lines connect clusters at different levels of the hierarchy. The height of each vertical line represents the distance between the merged clusters. By cutting the dendrogram at a certain height, you can determine the number of clusters you want to extract from the hierarchy.

Remember that dendrograms are a valuable tool for understanding the hierarchical structure of your data and making decisions about the appropriate number of clusters or granularity levels for clustering.