Physical fuzzy rule based unsupervised news article clustering

Abstract

This study introduces the Fuzzy-Rule-Enhanced Autoencoder (FREA), a novel hybrid approach for interpretable text clustering that addresses the limitations of traditional methods in comprehending nuanced linkages and incorporating domain-specific knowledge. FREA integrates fuzzy physical rules generated by Large Language Models (LLMs) that derive weighted related terms for provisional news labels within an autoencoder framework. This approach enhances interpretability and flexibility by including these principles into the feature learning process, aligning them with Word2Vec and RoBERTa embeddings to ensure contextually relevant and semantically robust representations. The fuzzy rule layer adaptively adjusts feature learning based on the similarity of inputs to predefined linguistic rules, allowing the model to incorporate domain-specific constraints in a completely trainable manner. FREA demonstrates its effectiveness and adaptability through evaluations on benchmark datasets utilizing both hard and soft clustering methods, along with different quantitative evaluation metrics, primarily focusing on news articles as the primary test case. This study amalgamates the benefits of fuzzy rules and deep learning, offering a flexible, interpretable, and human-aligned methodology for unsupervised clustering problems, therefore effectively linking rule-based reasoning with neural networks.