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An Improved Artificial Immune Network Based on the Secondary Immune Mechanism for Data Clustering

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Cloud Computing and Security (ICCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10602))

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Abstract

Data clustering is a typical method in data mining. As a effective algorithm for clustering, the Artificial Immune Network is inspired by natural immune system can reflect the structure of the given dataset, filter redundancy and cluster datasets without the number of clusters, so far it is widely used. However, it can’t effectively identify the noise nodes, the running time is long and too much parameters are set in improved algorithms. In order to shorten running time and reduce the impact of parameters, this paper proposes an improved artificial immune network based on the secondary immune mechanism. The Clone operator and Mutation operator are replaced by Competition Selection operator and Competition Selection strategy, which are inspired by the resource limited artificial immune system. Because the algorithm can reach a stable convergence only through two times, so it greatly reduce the running time; and can effectively identify the noise nodes due to the introduction of stimulation level. A number of datasets including artificial datasets and real-world datasets are used to evaluate the performance of the proposed algorithm and the other existing clustering algorithms, such as K-means, FCM, SC, aiNet and FCAIN. The simulation results indicate that the proposed artificial immune network algorithm is an effective and efficient method in data clustering.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61272279, 61272282, 61371201, and 61203303), the National Basic Research Program (973 Program) of China (No. 2013CB329402), the Program for Cheung Kong Scholars and Innovative Research Team in University (No. IRT_15R53), and the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an, 710071, Shanxi, China

    Yangyang Li, Xiaoju Hou & Licheng Jiao

  2. School of Computer and Software, Nanjing University of Information Science and Technology (NUIST), Nanjing, China

    Yu Xue

Authors
  1. Yangyang Li
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  2. Xiaoju Hou
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  3. Licheng Jiao
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  4. Yu Xue
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Corresponding author

Correspondence to Yangyang Li .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. National Dong Hwa University, Shoufeng, Taiwan

    Han-Chieh Chao

  3. China Information Technology Security Evaluation Center, Nanjing, China

    Xingang You

  4. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Li, Y., Hou, X., Jiao, L., Xue, Y. (2017). An Improved Artificial Immune Network Based on the Secondary Immune Mechanism for Data Clustering. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10602. Springer, Cham. https://doi.org/10.1007/978-3-319-68505-2_45

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  • DOI: https://doi.org/10.1007/978-3-319-68505-2_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68504-5

  • Online ISBN: 978-3-319-68505-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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