An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets

• URL: http://arxiv.org/abs/2502.16139v1
• Date: Sat, 22 Feb 2025 08:28:41 GMT
• Title: An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets
• Authors: Vijay Kumar Sutrakar, Nikhil Mogre,
• Abstract summary: We present an improved clustering technique for large textual datasets by leveraging fine-tuned word embeddings.<n>We show significant improvements in clustering metrics such as silhouette score, purity, and adjusted rand index (ARI)<n>The proposed technique will help to bridge the gap between semantic understanding and statistical robustness for large-scale text-mining tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, an improved clustering technique for large textual datasets by leveraging fine-tuned word embeddings is presented. WEClustering technique is used as the base model. WEClustering model is fur-ther improvements incorporating fine-tuning contextual embeddings, advanced dimensionality reduction methods, and optimization of clustering algorithms. Experimental results on benchmark datasets demon-strate significant improvements in clustering metrics such as silhouette score, purity, and adjusted rand index (ARI). An increase of 45% and 67% of median silhouette score is reported for the proposed WE-Clustering_K++ (based on K-means) and WEClustering_A++ (based on Agglomerative models), respec-tively. The proposed technique will help to bridge the gap between semantic understanding and statistical robustness for large-scale text-mining tasks.

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