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RETRACTED ARTICLE: Fuzzy clustering-based skyline query preprocessing algorithm for large-scale flow data analysis

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This article was retracted on 12 February 2024

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Abstract

In order to improve the effectiveness of the large-scale stream data skyline query preprocessing algorithm, a large-scale stream data skyline query preprocessing algorithm based on fuzzy clustering analysis (kdStreamSky) is proposed in the paper. Firstly, relevant concept of the large-scale stream data skyline query preprocessing algorithm was described; then, in order to solve the problem—curse of data dimensionality of the large-scale stream data skyline query preprocessing algorithm, the corresponding data subset was constructed and meanwhile MapReduce calculation model was combined to realize the mean value clustering center selection for the parallel mobile social network; meanwhile, in order to improve algorithm stability and facilitate the design of information forwarding scheme, a distributed hierarchical clustering method was adopted for the clustering analysis of individuals and the design of hierarchical forwarding scheme; and finally, the corresponding simulation experiment was implemented to verify algorithm effectiveness.

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Acknowledgements

National Natural Science Foundation of China (Grant No. 61472126).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan Province, China

    Yifu Zeng, Yantao Zhou & Fei Zheng

  2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan Province, China

    Xu Zhou

Authors
  1. Yifu Zeng
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  2. Yantao Zhou
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  3. Xu Zhou
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Corresponding author

Correspondence to Yifu Zeng.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11227-024-05979-0

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Zeng, Y., Zhou, Y., Zhou, X. et al. RETRACTED ARTICLE: Fuzzy clustering-based skyline query preprocessing algorithm for large-scale flow data analysis. J Supercomput 76, 1321–1330 (2020). https://doi.org/10.1007/s11227-018-2523-2

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  • DOI: https://doi.org/10.1007/s11227-018-2523-2

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