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CLUSTER INSIGHT: A WEIGHTED CLUSTERING TOOL FOR LARGE TEXTUAL DATA EXPLORATION

May 27, 2025
Amine Ferdjaoui

In unsupervised learning, the exploration of large volumes of textual data is a topic of significant interest. In this article, we present our compact and easy-to-use application to explore large volumes of textual data using clustering and generative models. We demonstrate how to adapt the Lasso weighted k-means algorithm to handle textual data. In addition, we present in detail a user-friendly package that shows how to use LLMs effectively to describe document classes.

Read the full article here.

Demonstration video of the associated web application


About the author

PhD student in Data Science | France
Passionate about machine learning, I hold master’s degrees in machine learning and MIAGE, with two years of experience as a data scientist and software developer. I’m currently pursuing a PhD with SogetiLabs and the Borelli Center.

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