Acta Geographica Sinica

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Spatial Clustering Method Based on General Multidimensional Cloud Model

DENG Yu1,2,  LIU Shenghe1,  ZHANG Wenting3,  WANG Li2,4,5,  WANG Jianghao1,2   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3. School of Resources and Environment Science, Wuhan University, Wuhan 430079, China;
    4. Institute of Policy and Management, CAS, Beijing 100190, China;
    5. Center for Interdisciplinary Studies of Natural and Social Sciences, CAS, Beijing 100190, China
  • Received:2009-01-09 Revised:2009-09-05 Online:2009-12-25 Published:2010-03-31
  • Supported by:

    National Natural Science Foundation of China, No.40971102; No.40871179; The CAS Special Grant for Postgraduate Research, Innovation and Practice

Abstract:

Traditional spatial clustering methods can not avoid the disadvantage of "hardware division", and can not describe the physical characteristics of spatial entity effectively. One-dimensional cloud model can not accurately reflect multi-attribute characteristics of the real-world. Besides, essential information of spatial objects might be lost during procedure of simple fusion. Standard two-dimensional cloud model overcomes some shortcomings of one-dimensional cloud, but it still can not meet the needs of simulating the non-homogeneous and non-symmetry characteristics of complex geographical phenomena. In view of the above, this paper sets forth a general multi-dimensional cloud model, which describes the characteristics of spatial objects more reasonably. Based on the empirical research, a detailed interpretation of clustering results is made from the spatial distribution of membership degree of clustering, the comparative study of Fuzzy C-means and a coupled analysis of residential land prices. It is found that general multi-dimensional cloud model can reflect the integrated characteristics of spatial objects better, reveal the spatial distribution of potential information, and realize spatial division more accurately in complex circumstances. However, due to the complexity of spatial interactions among geographical entities, the construction of cloud model is a specific and challenging task.

Key words: multi-dimensional cloud, spatial clustering, data mining, membership degree