待翻译：Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM

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--> [Submitted on 19 May 2026] Title:Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM View a PDF of the paper titled Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM, by Noor Khalal and 3 other authors View PDF HTML (experimental) Abstract:In Natural Language Processing (NLP), dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not adequately capture minority topics. To tackle this challenge, our paper presents a novel unsupervised data augmentation method that integrates Gaussian Mixture Models (GMMs) and Large Language Models (LLMs). Due to their flexibility and robustness, GMMs can detect clusters corresponding to underrepresented areas in the data, while LLMs create synthetic documents to enrich these clusters and improve their representation. Experiments on various imbalanced text datasets demonstrate that our approach preserves clustering performance in all cases and often enhances cluster interpretability, offering a robust and scalable solution for improving data representation in unsupervised NLP tasks. Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2607.28635 [cs.CL] (or arXiv:2607.28635v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.28635 arXiv-issued DOI via DataCite Journal reference: Advances in Intelligent Data Analysis: 23rd International Symposium on Intelligent Data Analysis; IDA 2025; Proceedings; pp 246-260 Related DOI: https://doi.org/10.1007/978-3-031-91398-3 DOI(s) linking to related resources Submission history From: Noor Khalal [view email] [v1] Tue, 19 May 2026 08:33:01 UTC (1,640 KB) Full-text links: Access Paper: View a PDF of the paper titled Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM, by Noor Khalal and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-07 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)