TextClusterLab: An Integrated Framework for Reliable Text Clustering Studies
In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems. However, evaluating text clustering algorithms remains challenging since many real-world textual datasets are not suitable for clustering assessment due to ambiguous semantic boundaries, the high dimensionality of embeddings, and inconsistent cluster structure. Current clustering dataset generators are designed for numerical data, providing limited support for text-specific benchmarking. This paper introduces TextClusterLab, a comprehensive framework for text clustering research. TextClusterLab offers a Large Language Model (LLM)-driven te...
| Authors | Daoming Wan, Yizheng Huang, Jimmy X. Huang |
|---|---|
| Primary category | cs.IR |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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