ERASMO: Leveraging Large Language Models for Enhanced Clustering Segmentation
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866929698016067584 |
|---|---|
| author | Silva, Fillipe dos Santos Kakimoto, Gabriel Kenzo Reis, Julio Cesar dos Reis, Marcelo S. |
| author_facet | Silva, Fillipe dos Santos Kakimoto, Gabriel Kenzo Reis, Julio Cesar dos Reis, Marcelo S. |
| contents | Cluster analysis plays a crucial role in various domains and applications, such as customer segmentation in marketing. These contexts often involve multimodal data, including both tabular and textual datasets, making it challenging to represent hidden patterns for obtaining meaningful clusters. This study introduces ERASMO, a framework designed to fine-tune a pretrained language model on textually encoded tabular data and generate embeddings from the fine-tuned model. ERASMO employs a textual converter to transform tabular data into a textual format, enabling the language model to process and understand the data more effectively. Additionally, ERASMO produces contextually rich and structurally representative embeddings through techniques such as random feature sequence shuffling and number verbalization. Extensive experimental evaluations were conducted using multiple datasets and baseline approaches. Our results demonstrate that ERASMO fully leverages the specific context of each tabular dataset, leading to more precise and nuanced embeddings for accurate clustering. This approach enhances clustering performance by capturing complex relationship patterns within diverse tabular data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ERASMO: Leveraging Large Language Models for Enhanced Clustering Segmentation Silva, Fillipe dos Santos Kakimoto, Gabriel Kenzo Reis, Julio Cesar dos Reis, Marcelo S. Computation and Language Artificial Intelligence 68T50 (Natural language processing), 68T01 (General topics in artificial intelligence) Cluster analysis plays a crucial role in various domains and applications, such as customer segmentation in marketing. These contexts often involve multimodal data, including both tabular and textual datasets, making it challenging to represent hidden patterns for obtaining meaningful clusters. This study introduces ERASMO, a framework designed to fine-tune a pretrained language model on textually encoded tabular data and generate embeddings from the fine-tuned model. ERASMO employs a textual converter to transform tabular data into a textual format, enabling the language model to process and understand the data more effectively. Additionally, ERASMO produces contextually rich and structurally representative embeddings through techniques such as random feature sequence shuffling and number verbalization. Extensive experimental evaluations were conducted using multiple datasets and baseline approaches. Our results demonstrate that ERASMO fully leverages the specific context of each tabular dataset, leading to more precise and nuanced embeddings for accurate clustering. This approach enhances clustering performance by capturing complex relationship patterns within diverse tabular data. |
| title | ERASMO: Leveraging Large Language Models for Enhanced Clustering Segmentation |
| topic | Computation and Language Artificial Intelligence 68T50 (Natural language processing), 68T01 (General topics in artificial intelligence) |
| url | https://arxiv.org/abs/2410.03738 |