Enhancing Document Clustering with Hybrid Recurrent Neural Networks and Autoencoders: A Robust Approach for Effective Semantic Organization of Large Textual Datasets

Dodda, Ratnam and Alladi, Suresh Babu (2024) Enhancing Document Clustering with Hybrid Recurrent Neural Networks and Autoencoders: A Robust Approach for Effective Semantic Organization of Large Textual Datasets. EAI Endorsed Transactions on Intelligent Systems and Machine Learning.

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Abstract

This research presents an innovative document clustering method that uses recurrent neural networks (RNNs) and autoencoders. RNNs capture sequential dependencies while autoencoders improve feature representation. The hybrid model, tested on different datasets (20-Newsgroup, Reuters, BBC Sports), out

Item Type: Article
Date Deposited: 04 Mar 2026 18:08
Last Modified: 11 Apr 2026 00:44
URI: http://eprints.eai.eu/id/eprint/51302

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