Introduction
This lab demonstrates how to perform hierarchical clustering with connectivity constraints using the Scikit-learn library in Python. In hierarchical clustering, clusters are formed by recursively merging or splitting them based on the distance between them. Connectivity constraints can be used to restrict the formation of clusters based on the connectivity between data points, which can result in more meaningful clusters.
VM Tips
After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.
Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.
If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
Skills Graph
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flowchart RL
sklearn(("`Sklearn`")) -.-> sklearn/CoreModelsandAlgorithmsGroup(["`Core Models and Algorithms`"])
sklearn(("`Sklearn`")) -.-> sklearn/UtilitiesandDatasetsGroup(["`Utilities and Datasets`"])
ml(("`Machine Learning`")) -.-> ml/FrameworkandSoftwareGroup(["`Framework and Software`"])
sklearn/CoreModelsandAlgorithmsGroup -.-> sklearn/cluster("`Clustering`")
sklearn/UtilitiesandDatasetsGroup -.-> sklearn/datasets("`Datasets`")
sklearn/CoreModelsandAlgorithmsGroup -.-> sklearn/neighbors("`Nearest Neighbors`")
ml/FrameworkandSoftwareGroup -.-> ml/sklearn("`scikit-learn`")
subgraph Lab Skills
sklearn/cluster -.-> lab-49331{{"`Hierarchical Clustering with Connectivity Constraints`"}}
sklearn/datasets -.-> lab-49331{{"`Hierarchical Clustering with Connectivity Constraints`"}}
sklearn/neighbors -.-> lab-49331{{"`Hierarchical Clustering with Connectivity Constraints`"}}
ml/sklearn -.-> lab-49331{{"`Hierarchical Clustering with Connectivity Constraints`"}}
end