World Expo 2010 Pavilions Clustering Analysis Based on Self-Organizing Map

LI Qianqian; GU Jifa

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World Expo 2010 Pavilions Clustering Analysis Based on Self-Organizing Map

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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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《系统科学与复杂性：英文版》 |2016年第4期|1089-1099|共11页
作者
LI Qianqian; GU Jifa;
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作者单位

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

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

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

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

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World Expo 2010 Pavilions Clustering Analysis Based on Self-Organizing Map

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