GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering

• URL: http://arxiv.org/abs/2608.07881v1
• Date: Sat, 08 Aug 2026 03:14:50 GMT
• Title: GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering
• Abstract summary: GRACE is a framework for scalable mixed-data clustering.<n>It maps heterogeneous values into knowledge-informed descriptions.<n>It achieves superior clustering accuracy and conceptual interpretability over 11 competing methods.
• Score: 19.352298911016668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discrete categorical symbols. Traditionally, algorithms rely entirely on dataset-internal statistics to estimate categorical relationships, which confines the learned metric to empirical co-occurrences and ignores conceptually obvious yet statistically unobserved affinities. Although LLMs offer external world knowledge, applying their text-centric reasoning to highly abstract tabular concepts presents significant challenges. Bridging this modality gap to construct a semantically complete metric typically requires embedding LLMs into iterative metric learning loops to dynamically optimize cross-modality representations. This incurs intractable computational overhead, forcing a compromise between semantic enrichment and scalability. Therefore, we propose GRACE, an LLM-grounded framework for scalable mixed-data clustering. GRACE shifts semantic acquisition to the attribute-value level via a multi-perspective LLM querying strategy, mapping heterogeneous values into knowledge-informed descriptions. Crucially, this one-shot grounding extracts general-purpose semantic representations that embed heterogeneous attributes into a unified space, decoupling expensive LLM invocation from iterative optimization. Furthermore, GRACE cross-validates these external semantics against dataset-internal statistical evidence to ensure alignment with the dataset-specific cluster structure. Ultimately, GRACE matches the scalability of conventional statistics-driven baselines while achieving superior clustering accuracy and conceptual interpretability over 11 competing methods. The source code is available at https://github.com/develop-yang/GRACE-GRACE-A

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