Abstract
In this digital era, grouping similar documents from the archives on the web is a difficult and computationally expensive task. In this paper, we propose an elitism based self-adaptive multi-population Poor and Rich optimization algorithm for grouping the similar documents, referred to as ESAMPRO. The objective function of the proposed work maximizes the accuracy and minimize the intra cluster distance. The proposed algorithm is evaluated using the various extrinsic cluster quality metrics. An in-depth analysis of the experimental results on four supervised benchmark datasets confirms that the proposed ESAMPRO algorithm outperformed the five well-known document clustering algorithms such as K-means, particle swarm optimization, whale optimization, dragonfly and grey wolf optimization algorithm.
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Acknowledgements
We would like to thank the anonymous reviewers for their helpful comments and advice in improving this work. Also, we would like to thank the Management and Principal of Mepco Schlenk Engineering College (Autonomous), Sivakasi for providing us the state of art facilities to carry out this proposed research work in the Mepco Research Centre in collaboration with Anna University Chennai, Tamil Nadu, India.
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Thirumoorthy, K., Muneeswaran, K. An elitism based self-adaptive multi-population Poor and Rich optimization algorithm for grouping similar documents. J Ambient Intell Human Comput 13, 1925–1939 (2022). https://doi.org/10.1007/s12652-021-02955-x
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DOI: https://doi.org/10.1007/s12652-021-02955-x