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World Expo 2010 pavilions clustering analysis based on self-organizing map

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Abstract

This paper reports the classification of 90 sample pavilions in Shanghai World Expo. An artificial intelligence based nonlinear clustering method known as Self-Organizing Map (SOM) has been used to classify expo pavilions. SOM is an efficient tool for visualization of multidimensional data. To conduct the classification, four characteristics namely Hurst exponent for queue length, Hurst exponent for waiting time, mean queue length and mean waiting time have been applied. The classification results show that Shanghai World Expo pavilions can be optimally classified into four classes. This result will shed light on further studies that how to manage the queue of World Expo pavilions in the future.

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Authors and Affiliations

  1. Institute of Policy and Management, Chinese Academy of Sciences, Beijing, 100190, China

    Qianqian Li

  2. Center for Interdisciplinary Studies of Natural and Social Sciences, Chinese Academy of Sciences, Beijing, 100190, China

    Qianqian Li & Jifa Gu

  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Jifa Gu

Authors
  1. Qianqian Li
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  2. Jifa Gu
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Corresponding author

Correspondence to Jifa Gu.

Additional information

This research was supported by 973 Research Program under Grant No. 2010CB731500, the National Natural Science Foundation of China under Grant Nos. 71403262, 91024010, 91324009, Innovative Team Program under Grant No. GH13041 and Major Program of Institute of Policy and Management, Chinese Academy of Sciences under Grant No. Y201201Z06.

This paper was recommended for publication by Editor ZHANG Xun.

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Li, Q., Gu, J. World Expo 2010 pavilions clustering analysis based on self-organizing map. J Syst Sci Complex 29, 1089–1099 (2016). https://doi.org/10.1007/s11424-015-4167-0

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  • DOI: https://doi.org/10.1007/s11424-015-4167-0

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