A Hybrid Deep Learning Framework for Automated Document Clustering and Intelligent Label Generation
Keywords:
Document clustering, deep learning, transformer models, label generation, natural language processing, unsupervised learningAbstract
The exponential growth of digital documents across various domains has necessitated the development of sophisticated automated systems for document organization and categorization. This paper presents a novel hybrid deep learning framework that combines unsupervised clustering techniques with intelligent label generation mechanisms to address the challenges of automated document classification. The proposed framework integrates transformer-based embeddings, hierarchical clustering algorithms, and neural language models to achieve superior performance in both clustering accuracy and interpretability. Our approach demonstrates significant improvements over traditional methods, achieving a silhouette score of 0.847 and normalized mutual information of 0.923 across diverse document corpora. The framework's ability to generate meaningful, human-interpretable labels for discovered clusters represents a substantial advancement in making automated document organization systems more practical and user-friendly. Experimental results on benchmark datasets including Reuters-21578, 20 Newsgroups, and custom enterprise document collections validate the effectiveness of our hybrid approach.
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