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Design and analysis of text document clustering using salp swarm algorithm

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Abstract

In the technological era, exponential increase of unorganized text documents offers increased difficulties retrieving the most relevant data. The document clustering is a most prominent technique that transforms unorganized contents into organized contents in the form of clusters. The recognition technique always undergoes clustering of text documents with misleading or redundant information that degrades document clustering quality. In this study, a salp swarm algorithm (SSA) is used for clustering the text documents. The study is improved with a similarity and a distance-based measurements as an objective function in the clustering domain. The experimental validation is conducted to show the efficacy of SSA-based similarity distance measurement that prominently improves the quality of clustering the text documents. The comparison with existing methods shows that the proposed SSA offers better clustering of text documents in accuracy, sensitivity, specificity, and f-measure.

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Authors and Affiliations

  1. Indian Institute of Information Technology, Kalyani, West Bengal, India

    Muruganantham Ponnusamy

  2. Department of Computer Science and Engineering, Galgotias University, Plot No. 2, Sector-17A, Yamuna Expressway, Greater Noida, 203201, Uttar Pradesh, India

    Pradeep Bedi

  3. St. Peter’s Institute of Higher Education and Research, Chennai, Tamilnadu, India

    Tamilarasi Suresh

  4. Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Aravindhan Alagarsamy

  5. Department of Computer Science and Information Technology, Post Doctoral Fellowship, Srinivas University, Karnataka, India

    R. Manikandan & N. Yuvaraj

Authors
  1. Muruganantham Ponnusamy
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  2. Pradeep Bedi
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  3. Tamilarasi Suresh
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  4. Aravindhan Alagarsamy
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  5. R. Manikandan
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  6. N. Yuvaraj
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Correspondence to Muruganantham Ponnusamy.

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Ponnusamy, M., Bedi, P., Suresh, T. et al. Design and analysis of text document clustering using salp swarm algorithm. J Supercomput 78, 16197–16213 (2022). https://doi.org/10.1007/s11227-022-04525-0

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  • DOI: https://doi.org/10.1007/s11227-022-04525-0

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