Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models

arXiv:2601.01162v3 Announce Type: replace Abstract: 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

Source: arXiv cs.LG — read the full report at the original publisher.

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