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Web Data Mining Algorithm in Cloud Service Architecture Based on New Popular Learning Algorithm and Adaptive Adjustment Mechanism

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Exploration of Novel Intelligent Optimization Algorithms (ISICA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1590))

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Abstract

Due to the complex distribution of web data and frequent updates under the cloud service architecture, it is easy to drive data disasters and data accidents. This leads to many problems in web data mining, such as how to choose the best algorithm to process the corresponding type of data, and how to effectively reduce the dimensionality of the web data in the cloud service architecture and process multiple target information and multiple customer needs at the same time. In order to overcome these problems, we transform the multi-information goal and multi-user demand (constraint conditions) in web data mining into a constrained multi-objective optimization model, and solve it by a constrained particle swarm multi-objective optimization algorithm. In order to make it easier for the constrained multi-objective particle swarm algorithm to solve different types of problems to find an effective solution set closer to the real Pareto front, a new manifold learning algorithm based on the constrained multi-objective particle swarm algorithm is built and used to solve this problem. Experiments results demonstrate that this can improve the service efficiency of cloud computing.

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Acknowledgement

This work was supported by Characteristic innovation projects of Department of Education of Guangdong Province under No. Grant KJ2021C014 and Science and Technology Ph.D. Research Startup Project, China (SZIIT2022KJ001).

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Authors and Affiliations

  1. Shenzhen Institute of Information Technology, Shenzhen, China

    Hui Wang, Tie Cai, Yinfeng Wang, Geng Yang & Junwei Liang

Authors
  1. Hui Wang
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  2. Tie Cai
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  3. Yinfeng Wang
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  4. Geng Yang
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  5. Junwei Liang
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Corresponding author

Correspondence to Tie Cai .

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Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. The University of Aizu, Aizu-Wakamatsu, Japan

    Yong Liu

  3. South China Agricultural University, Guangzhou, China

    Wenxiang Wang

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Wang, H., Cai, T., Wang, Y., Yang, G., Liang, J. (2022). Web Data Mining Algorithm in Cloud Service Architecture Based on New Popular Learning Algorithm and Adaptive Adjustment Mechanism. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_46

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  • DOI: https://doi.org/10.1007/978-981-19-4109-2_46

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4108-5

  • Online ISBN: 978-981-19-4109-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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