Document Clustering Web Service With Dendrograms and Linkage Options

Added Jun 2025 3 design docs

Media teams that need to organize large text collections rarely want a notebook — they want a tool: upload documents, choose settings, download organized results. This project builds that tool around hierarchical clustering, the technique of choice when the number of natural groups is unknown in advance. The intern develops a Flask web application where users upload a set of text documents, which are vectorized and clustered with agglomerative methods from scikit-learn. The interface offers a choice of linkage strategies — ward, complete, and average — and renders dendrograms so users can inspect the merge structure and pick a cut point informed by the data rather than a guess. Cluster assignments are exportable as files, with pandas and NumPy managing the data handling, so the results feed directly into downstream editorial or archival workflows. The intern learns text feature engineering, the mechanics and trade-offs of linkage criteria, and dendrogram-driven cluster selection, while practicing web development with Flask — delivering an unsupervised learning capability as a finished, self-service product rather than an analysis that lives on one laptop.

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