@article{10.11648/j.mlr.20251001.14,
  author = {Vijay Kumar Sutrakar and Nikhil Mogre},
  title = {An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets
},
  journal = {Machine Learning Research},
  volume = {10},
  number = {1},
  pages = {32-43},
  doi = {10.11648/j.mlr.20251001.14},
  url = {https://doi.org/10.11648/j.mlr.20251001.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20251001.14},
  abstract = {In the rapid growth of textual data in various domains has increased the need for efficient clustering techniques capable of handling large-scale datasets. Traditional clustering methods often fail to capture semantic relationships and struggle with high-dimensional, sparse data. The present study shows an improved document clustering technique, i.e., WEClustering++, which enhances the existing WEClustering framework by integrating fine-tuned BERT based word embeddings. The proposed model incorporates advanced dimensionality reduction techniques and optimized clustering algorithms to improve clustering accuracy. In the present work, the BERT-large model, fine-tuned on domain-specific datasets is utilized. Seven benchmark datasets spanning various domains and sizes are considered. These datasets include collections of research articles, news articles, and other domain-specific texts. Experimental evaluations on multiple benchmark datasets demonstrate significant performance improvements in clustering metrics, including silhouette score, purity, and ARI. Results show a 45% and 67% increase in median silhouette scores for WEClustering_K++ (K-means-based) and WEClustering_A++ (Agglomerative-based) models, respectively. Result also shows an increase of median purity metrics of 0.4% and 0.8% is obtained for proposed WEClustering_K++ and WEClustering_A++ compared to the state of art model. Also, an increase of median ARI metrics of 7% and 11% is obtained for proposed WEClustering_K++ and WEClustering_A++ compared to the state of art model. These findings highlight the potential of fine-tuned word embeddings in bridging the gap between statistical clustering robustness and semantic understanding. The proposed approach is expected to contribute to advancements in large-scale text mining applications, including document organization, topic modelling, and information retrieval.
},
 year = {2025}
}

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TY  - JOUR
T1  - An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets

AU  - Vijay Kumar Sutrakar
AU  - Nikhil Mogre
Y1  - 2025/04/29
PY  - 2025
N1  - https://doi.org/10.11648/j.mlr.20251001.14
DO  - 10.11648/j.mlr.20251001.14
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 32
EP  - 43
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20251001.14
AB  - In the rapid growth of textual data in various domains has increased the need for efficient clustering techniques capable of handling large-scale datasets. Traditional clustering methods often fail to capture semantic relationships and struggle with high-dimensional, sparse data. The present study shows an improved document clustering technique, i.e., WEClustering++, which enhances the existing WEClustering framework by integrating fine-tuned BERT based word embeddings. The proposed model incorporates advanced dimensionality reduction techniques and optimized clustering algorithms to improve clustering accuracy. In the present work, the BERT-large model, fine-tuned on domain-specific datasets is utilized. Seven benchmark datasets spanning various domains and sizes are considered. These datasets include collections of research articles, news articles, and other domain-specific texts. Experimental evaluations on multiple benchmark datasets demonstrate significant performance improvements in clustering metrics, including silhouette score, purity, and ARI. Results show a 45% and 67% increase in median silhouette scores for WEClustering_K++ (K-means-based) and WEClustering_A++ (Agglomerative-based) models, respectively. Result also shows an increase of median purity metrics of 0.4% and 0.8% is obtained for proposed WEClustering_K++ and WEClustering_A++ compared to the state of art model. Also, an increase of median ARI metrics of 7% and 11% is obtained for proposed WEClustering_K++ and WEClustering_A++ compared to the state of art model. These findings highlight the potential of fine-tuned word embeddings in bridging the gap between statistical clustering robustness and semantic understanding. The proposed approach is expected to contribute to advancements in large-scale text mining applications, including document organization, topic modelling, and information retrieval.

VL  - 10
IS  - 1
ER  - 

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