Hierarchical Clustering Explorer for News and Media Archives

Added Jun 2025 3 design docs

Editors and content managers at media organizations sit on archives with thousands of articles, transcripts, and posts, and grouping related material by hand simply does not scale. Unsupervised learning offers a way to surface structure in that text without labeling a single document first, and hierarchical clustering is especially useful because it does not force anyone to guess the number of topics in advance. The intern builds a Streamlit application that takes a collection of text documents, converts them into numerical feature vectors, and applies agglomerative hierarchical clustering from scikit-learn to group similar content. SciPy powers the dendrogram rendering, so users can see exactly how documents merge into clusters step by step and choose a sensible cut-off depth visually rather than arbitrarily. The app lets users experiment with different cluster counts and immediately see how the groupings reorganize. The project gives the intern a practical introduction to text vectorization, distance-based clustering, and dendrogram interpretation, along with experience packaging an unsupervised learning workflow into an interactive Python web app that a non-programmer could use to explore a content archive.

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