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Dynamic k-means clustering for risk decision making and its application to China's customs targeting

Published: 07 August 2012 Publication History

Abstract

Customs targeting is a typical risk decision making problem. In this problem, the empirical smuggling probability density function of import/export goods is needed for targeting decision. Generally, the density function can be obtained by applying statistical analysis, especially regression analysis, to historical observations (samples). A critical presumption is that the samples are homogeneous, which means they are drawn from the same distribution. Therefore, clustering techniques are usually employed as the preprocessing methods in statistical analysis. However, in China's customs targeting problem, severe heterogeneity and abnormality exist in the historical observations, which makes the conventional clustering methods inapplicable for preprocessing. In this paper, we develop a dynamic K-means clustering approach to solve this problem. Through optimizing a validity function of clustering, the proposed approach divides the entire samples into different clusters iteratively. As the result of this preprocessing technique, samples in the same cluster are more compact, while in different clusters are more discriminated. Based on the proposed dynamic clustering approach, we develop a risk decision making process. Application to China's customs targeting problem indicates that the proposed approach could increase the efficiency of customs targeting decision.

References

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Hua, Z. S., Zhang, B., Yang, J., and Tan, D. S. 2007. A new approach of forecasting intermittent demand for spare parts inventories in the process industries. J. Oper. Res. Soc. 58 (1), 52--61.
[2]
Hua, Z. S., Li, S. J., and Tao, Z. 2006. A rule-based risk decision making approach and its application in China's Customs inspection decision. J. Oper. Res. Soc. 57 (11), 1313--1322.
[3]
Sfetsos, A. 2003. Short-term load forecasting with a hybrid clustering algorithm. In IEE Proceedings on Generation, Transmission and Distribution. 150 (3), 257--262.
[4]
Wu, K. L., and Yang, M. S. 2005. A cluster validity index for fuzzy clustering. Pattern Recogn. Lett. 26 (9), 1275--1291.

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  1. Dynamic k-means clustering for risk decision making and its application to China's customs targeting

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    Published In

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    ICEC '12: Proceedings of the 14th Annual International Conference on Electronic Commerce
    August 2012
    357 pages
    ISBN:9781450311977
    DOI:10.1145/2346536

    Sponsors

    • Singapore Management University: Singapore Management University

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 August 2012

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    Author Tags

    1. clustering
    2. customs targeting
    3. risk decision making
    4. statistical analysis

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    ICEC '12
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    • Singapore Management University

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    Overall Acceptance Rate 150 of 244 submissions, 61%

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