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Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms outperform K-means for text document clustering. PSO demonstrated the best performance in finding optimal solutions for complex clustering tasks.

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Computer Science

Background:

  • Text document clustering is an unsupervised method for grouping documents by content similarity.
  • Swarm intelligence (SI) algorithms offer flexible, robust, and decentralized solutions for complex problems.
  • Traditional clustering algorithms may struggle with the nuances of text data.

Purpose of the Study:

  • To compare the performance of different swarm intelligence algorithms for text document clustering.
  • To identify the most effective SI algorithm for optimizing search and extracting information from text data.

Main Methods:

  • Comparative analysis of Particle Swarm Optimization (PSO), bat algorithm, Grey Wolf Optimization (GWO), and K-means.
  • Experiments conducted on six diverse datasets derived from BBC Sport news and 20 newsgroups.
  • Evaluation of algorithm performance based on clustering accuracy and solution optimality.

Main Results:

  • Both PSO and GWO demonstrated superior performance compared to K-means in text document clustering.
  • PSO consistently achieved the best results, indicating its effectiveness in finding optimal clustering solutions.
  • Algorithm performance varied, highlighting the importance of algorithm selection based on specific dataset characteristics.

Conclusions:

  • Swarm intelligence algorithms, particularly PSO and GWO, are highly suitable for complex text document clustering tasks.
  • PSO emerges as the top-performing algorithm for text document clustering, offering robust and optimal solutions.
  • The study provides valuable insights for selecting appropriate SI algorithms in information retrieval and data analysis applications.