Comparative Analysis of K-Means Algorithm and Particle Swarm Optimization for Search Result Clustering

  • Shashi MehrotraEmail author
  • Aditi Sharan
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)


Clustering is being used to organize search results into clusters with an aim to help a user in accessing relevant information. The paper performs a comparative analysis of the most common traditional clustering algorithms: k-means and nature-inspired algorithm, and Particle Swarm Optimization (PSO). Experiments are conducted over the well-known dataset, AMBIENT, used for topic clustering. Experimental results show the highest recall and F-measure is achieved by the PSO. Though the highest precision is achieved by the k-means algorithm, in most of the topics, PSO shows a better result than the k-means algorithm.


Clustering K-means algorithm Particle Swarm Optimization Polysemy 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKoneru Lakshmaiah Education FoundationGunturIndia
  2. 2.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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