Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration - Université de Paris - Faculté des Sciences
Communication Dans Un Congrès Année : 2025

Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration

Séverine Affeldt
Mohamed Nadif

Résumé

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.
CCS Concepts

• Computing methodologies → Cluster analysis; Probabilistic reasoning; Natural language processing; • Mathematics of computing → Probabilistic algorithms.

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Dates et versions

hal-04859428 , version 1 (30-12-2024)

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Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif. Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration. The 18th ACM International Conference on Web Search and Data Mining, Mar 2025, Hannover, Germany. ⟨10.1145/3701551.3704127⟩. ⟨hal-04859428⟩
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