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International Conference of Pioneering Computer Scientists, Engineers and Educators

ICPCSEE 2017: Data Science pp 516–527Cite as

Data Clustering Algorithm Based on Artificial Immune Network

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 728))

Abstract

For the problem that the termination condition of artificial immune network algorithm aiNet is difficult to determine, an intelligent artificial immune network algorithm S-aiNet is proposed. The S-aiNet determines whether the network is saturated by monitoring the change trend of new generation population in the iterative process according to the affinity of the new generation of network cells and existing cells. The algorithm improves the adaptability of aiNet and reduces the number of parameters. For the problem that the network of aiNet updates slowly, a regional search optimization algorithm AS-aiNet is proposed. The AS-aiNet equally divides the antibody space where the network cells and antigen located, and only searches the antibody cells located in the same region as antigens in the immune response. The AS-aiNet reduces the workload of search in the process of immune response and effectively enhances the time efficiency of algorithm operation. Adopting public data set, experiments show that the time efficiency of AS-aiNet is 10% better than that of aiNet.

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References

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Acknowledgements

This work was supported by National Natural Science Foundation of China (U1433116), Foundation of Graduate Innovation Center in NUAA (kfjj20171603).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Street, Nanjing, 211106, Jiangsu, People’s Republic of China

    Zongkun Li & Dechang Pi

Authors
  1. Zongkun Li
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  2. Dechang Pi
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Corresponding author

Correspondence to Dechang Pi .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Beiji Zou

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin University of Science and Technology, Harbin, China

    Guanglu Sun

  4. Northeast Forestry University, Harbin, China

    Weipeng Jing

  5. Huaihua University, Huaihua, Hunan, China

    Xiaoning Peng

  6. Sciences of Country Tripod Institute of Data Science, Harbin, China

    Zeguang Lu

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© 2017 Springer Nature Singapore Pte Ltd.

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Cite this paper

Li, Z., Pi, D. (2017). Data Clustering Algorithm Based on Artificial Immune Network. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_44

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  • DOI: https://doi.org/10.1007/978-981-10-6388-6_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6387-9

  • Online ISBN: 978-981-10-6388-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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