用大模型给分类数据加语义特征，提升聚类效果
Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
- 用外部知识库为每个分类值生成语义嵌入，增强相似性判断
- 在8个基准数据集上平均ARI得分排名1.3，优于7个对比方法
- 适合小样本场景下的医疗、营销等分类数据聚类任务
定性数据广泛存在于医疗、营销和生物信息学等领域，聚类是发现模式的基础工具。定性数据聚类的核心难点在于属性值之间缺乏固有顺序或距离度量。现有方法通常依赖数据集内共现统计来恢复关系，但当样本量较小时，该方法不可靠，且未充分利用每个值的语义上下文。为此，本文提出BREVE（基于外部值增强的平衡表示）聚类框架，通过外部知识库为每个定性值增加额外的语义维度，生成密集嵌入以编码其语义内容。为避免原始值身份被稀释，还添加轻量级独热组件。一个由聚类紧凑性引导的自适应权重，决定增强维度在最终表示中的贡献强度。在8个基准数据集上的实验显示，平均ARI排名达1.3，优于7个代表性对比方法。
原文摘要 · Abstract (English)
Qualitative data are widespread in domains such as healthcare, marketing, and bioinformatics, where clustering offers a fundamental tool for pattern discovery. A core difficulty of qualitative-data clustering lies in measuring similarity among attribute values that carry no inherent ordering or distance. To recover such relationships, existing studies typically rely on within-dataset co-occurrence statistics. This statistical route, however, becomes unreliable once the sample size is small, and the semantic context of each value is therefore left underexploited. Motivated by this limitation, this paper proposes BREVE (Balanced Representation via External Value Enrichment), a clustering framework that enriches each qualitative value with extra semantic dimensions drawn from an external knowledge base. That is, every unique value is expanded by a dense embedding that encodes its semantic content. To prevent the original value identity from being diluted by the added dimensions, a lightweight one-hot component is further appended. An adaptive weight, guided by cluster compactness, then determines how strongly the enrichment dimensions enter the final representation. With this design, experiments on eight benchmark datasets yield an average ARI rank of 1.3 against seven representative competitors.
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