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Optimization of scientific publications clustering with ensemble approach for topic extraction

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Abstract

The continually developing Internet generates a considerable amount of text data. When attempting to extract general topics or themes from a massive corpus of documents, dealing with such a large volume of text data in an unstructured format is a big problem. Text document clustering (TDC) is a technique for grouping texts based on their content similarity. Partitioning text collection based on the documents’ content significance is one of the most challenging tasks at TDC. This study proposes the Bare-Bones Based Salp Swarm Algorithm (BBSSA) to solve the problem of TDC. In addition, to extract the topics from the clusters, an ensemble approach for automatic topic extraction (TE) is proposed. The proposed BBSSA and the ensemble TE approach are tested using six standard benchmarks and six scientific publishing datasets from top QS ranking UAE universities. BBSSA’s findings are compared with sixteen well-known techniques, including eleven metaheuristic algorithms, such as the Whale Optimization Algorithm (WOA), Firefly Algorithm (FFA), Bat Algorithm (BAT), Harmony Search (HS), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Multi-Verse Optimizer (MVO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Krill Herd Algorithm (KHA), SSA, and five clustering methods, such as K-means++, K-means, Density-based Spatial Clustering of Applications with Noise (DBSCAN), Spectral, and Agglomerative. The results of the ensemble TE approach are compared with those of seven well-known statistical methods, including Mutual Information (MI), TextRank (TR), Co-Occurrence Statistical Information-based Keyword Extraction (CSI), Term Frequency-Inverse Document Frequency (TF-IDF), most frequent based keyword extraction (TF), YAKE!, and RAKE. According to the experiments, the BBSSA outperforms all other approaches and is exceedingly competitive. The results also reveal that for most datasets, the proposed ensemble TE strategy outperforms all existing TE methods based on external metrics. Thus, the ensemble TE approach can be seen as a supplement to the other methods.

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Acknowledgements

This work was supported by grant from the Deanship of Graduate Studies and Research (DGSR) at Ajman University, Ajman, UAE (Grant No. 2021-IRG-ENIT-6).

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Authors and Affiliations

  1. College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Mohammed Azmi Al-Betar, Ghazi Al-Naymat & Kamran Arshad

  2. Department of Information Technology, Al-Huson University College, Al-Huson, Jordan

    Mohammed Azmi Al-Betar

  3. Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates

    Ammar Kamal Abasi

  4. Artificial Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates

    Mohammed Azmi Al-Betar, Ghazi Al-Naymat, Kamran Arshad & Sharif Naser Makhadmeh

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Appendix

Appendix

Minkowski distances:

$$\begin{aligned} D(d_1,d_2)=\displaystyle \left( \sum _{i=1}^{n}|t_{1,i} - t_{2,i}|^p \right) ^{1/p} \end{aligned}$$

See Tables 10, 11, 12 and 13.

Table 10 Term weight using TF-IDF
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Table 11 Clusters centroids
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Table 12 Example of \(a_{ki}\) where the \(k=1\)
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Table 13 Total average distance of the documents from the cluster centroid (ADDC)
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Al-Betar, M.A., Abasi, A.K., Al-Naymat, G. et al. Optimization of scientific publications clustering with ensemble approach for topic extraction. Scientometrics 128, 2819–2877 (2023). https://doi.org/10.1007/s11192-023-04674-w

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