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Web Document Clustering by Using PSO-Based Cuckoo Search Clustering Algorithm

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Recent Advances in Swarm Intelligence and Evolutionary Computation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 585))

Abstract

With the increasing amount of web information, web document clustering plays an important role in Information Retrieval. This paper presents a PSO-based Cuckoo Search Clustering Algorithm to combine the strengths of Cuckoo Search and Particle Swarm. The solutions of new cuckoos are based on the solutions of PSO. Among these solutions, the algorithm will replace some eggs on lack of fitness with successful solutions until an optimal solution emerges. The proposed hybrid algorithm is tested with a web document benchmark dataset and the results show that it performs well in web document clustering area.

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Author information

Authors and Affiliations

  1. University of Technology (Yatanarpon Cyber City), Pyin Oo Lwin, Myanmar

    Moe Moe Zaw & Ei Ei Mon

Authors
  1. Moe Moe Zaw
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  2. Ei Ei Mon
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Correspondence to Moe Moe Zaw .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

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Zaw, M.M., Mon, E.E. (2015). Web Document Clustering by Using PSO-Based Cuckoo Search Clustering Algorithm. In: Yang, XS. (eds) Recent Advances in Swarm Intelligence and Evolutionary Computation. Studies in Computational Intelligence, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-319-13826-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-13826-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13825-1

  • Online ISBN: 978-3-319-13826-8

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