Abstract
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.
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Mehrotra, S., Sharan, A. (2020). Comparative Analysis of K-Means Algorithm and Particle Swarm Optimization for Search Result Clustering. In: Zhang, YD., Mandal, J., So-In, C., Thakur, N. (eds) Smart Trends in Computing and Communications. Smart Innovation, Systems and Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-0077-0_12
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DOI: https://doi.org/10.1007/978-981-15-0077-0_12
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