Journal of Geographical Sciences

, Volume 20, Issue 5, pp 787–798 | Cite as

General multidimensional cloud model and its application on spatial clustering in Zhanjiang, Guangdong

  • Yu Deng
  • Shenghe Liu
  • Wenting Zhang
  • Li Wang
  • Jianghao Wang


Traditional spatial clustering methods have the disadvantage of “hardware division“, and can not describe the physical characteristics of spatial entity effectively. In view of the above, this paper sets forth a general multi-dimensional cloud model, which describes the characteristics of spatial objects more reasonably according to the idea of non-homogeneous and non-symmetry. Based on infrastructures’ classification and demarcation in Zhanjiang, 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. General multi-dimensional cloud model reflects the integrated characteristics of spatial objects better, reveals the spatial distribution of potential information, and realizes spatial division more accurately in complex circumstances. However, due to the complexity of spatial interactions between geographical entities, the generation of cloud model is a specific and challenging task.


multi-dimensional cloud spatial clustering data mining membership degree Zhanjiang 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ankerst M, Breunig M M, Kriegel H P et al., 1999. OPTICS: Ordering points to identify the clustering structure. In: Proc. ACMSIGM OD’99 Int. Conf. on Management of Data, Philadephia PA, 1999.Google Scholar
  2. Berkhin P, 2000. Survey of clustering data mining techniques. Accrue Software.Google Scholar
  3. Bezdek J C, 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press.Google Scholar
  4. Chen Huilin, 1998. A fuzzy comprehensive analysis of the resource-environment consciousness of the people in Mashan region of Guizhou Province. Scientia Geographica Sinica, 18(4): 379–386. (in Chinese)Google Scholar
  5. Di Kaichang, Li Deyi, Li Deren, 1999. Cloud theory and its applications in spatial data mining and knowledge discovery. Journal of Image and Graphics, 11(4): 930–935. (in Chinese)Google Scholar
  6. Ester M, Kriegel H P, Sander J et al., 1996. A density-based algorithm for discovering clusters in large spatial databases. In: Proc. 1996 Int. Con f. Knowledge Discovery and Data Mining (KDD’96), 1996: 226–331.Google Scholar
  7. Hou Yingzi, Chen Xiaoling, Wang Fangxiong, 2008. Fuzzy comprehensive evaluation of water environment value based on GIS. Scientia Geographica Sinica, 28(1): 90–95. (in Chinese)Google Scholar
  8. Jiang Rong, Fan Jianhua, Li Deyi, 2000. Automatic generation of pan-concept-tree on numerical data. Chinese Journal of Computers, 23(5): 470–476. (in Chinese)Google Scholar
  9. Karypic G., Han E H, 1999. CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. Computer, 32: 68–75.CrossRefGoogle Scholar
  10. Kaufman L, Rousseeuw P J, 1990. Finding groups in data: An introduction to cluster analysis. Wiley & Sons.Google Scholar
  11. Li Deyi, 2000. Uncertainty in knowledge representation. Engineering Science, 2(10): 73–76. (in Chinese)Google Scholar
  12. Li D Y, Di K C, Li D E et al., 1998. Mining association rules with linguistic cloud models. In: PAKDD’98 Proc. of the Second Pacific-Asia Confon Knowledge Discovery and Data Mining. Melbourne, 1998: 392–394.Google Scholar
  13. Li D Y, Han J, Chan E et al., 1997. Knowledge representation and discovery based on linguistic atoms. In: Proc of the 1st Pacific-Asia Conf. on KDD&DM, Singapore, 1997: 89–97.Google Scholar
  14. Li Deyi, Liu Changyu, 2004. Study on the universality of the normal cloud model. Engineering Science, 6(8): 29–33. (in Chinese)Google Scholar
  15. Li Deyi, Liu Changyu, Du Yu et al., 2004. Artificial intelligence with uncertainty. Journal of Software, 15(11): 1583–1594. (in Chinese)Google Scholar
  16. Li Deyi, Meng Haijun, Shi Xuemei, 1995. Membership cloud and membership cloud generator. Computer Research and Development, 32(6): 15–20. (in Chinese)Google Scholar
  17. Li Xinyun, Zheng Xinqi, 2004. On spatial clustering of combination of coordinate and attribute. Geography and Geoinformation Science, 3: 38–40. (in Chinese)Google Scholar
  18. Liu Changyu, 2005. Some statistical analysis of the normal cloud model. Information and Control, 2005, 4: 237–243. (in Chinese)Google Scholar
  19. Liu Changyu, Dai Xiaojun, Li Deyi, 2004. A new algorithm of backward cloud. Journal of System Simulation, 16(11): 2417–2420. (in Chinese)Google Scholar
  20. Liu Guihua, 2007. Research on association rules based on cloud mode [D]. Jinan: Shandong Normal University.Google Scholar
  21. Macqueen J, 1967. Some methods for classification and analysis of multivariate observations. California: Berkeley.Google Scholar
  22. Qin Kun, Li Deyi, Xu Kai, 2006. Image segmentation based on cloud model. Survey and Mapping Information Engineering, 31(5): 3–6. (in Chinese)Google Scholar
  23. Sheikholeslami G, Chatterjee S, Zhang A, 1998. Wave cluster: A multi-resolution clustering approach for very large spatial databases. In: Proc. 1998 Int. Conf. Very Large Data Bases (VLDB’98), 428–439.Google Scholar
  24. Tang Yijian, 1986. Regional river water quality fuzzy cluster analysis. Acta Geographica Sinica, 41(3): 234–241. (in Chinese)Google Scholar
  25. Wang H J, 2007a. Spatial clustering method based on cloud model and data field. Advances in Computation and Intelligence, 4683: 420–427.CrossRefGoogle Scholar
  26. Wang H J, 2007b. Spatial clustering method based on cloud model. Fuzzy Systems and Knowledge Discovery, 2: 272–276.Google Scholar
  27. Wang W, Yang J, Muntz R, 1997. STING: A statistical information grid approach to spatial data mining. In: Proc. 1997 Int. Con f. Very Large Data Bases (VLDB’97), Athens, Greece, 1997: 186–195.Google Scholar
  28. Zhang Guoying, Sha Yun, Liu Xuhong et al., 2004. High dimensional cloud model and its application in multiple attribute evaluation. Transactions of Beijing Institute of Technology, 12: 1065–1071. (in Chinese)Google Scholar
  29. Zhang T, Rarmak R, Livny M, 1996. BIRCH: An efficient data clustering method for very large data based. In: Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’96), Montreal, Canada, 1996: 103–114.Google Scholar

Copyright information

© Science in China Press and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yu Deng
    • 1
    • 4
  • Shenghe Liu
    • 1
  • Wenting Zhang
    • 2
  • Li Wang
    • 3
    • 4
  • Jianghao Wang
    • 1
    • 4
  1. 1.Institute of Geographic Sciences and Natural Resources ResearchCASBeijingChina
  2. 2.School of Resources and Environment ScienceWuhan UniversityWuhanChina
  3. 3.Institute of Policy and ManagementCASBeijingChina
  4. 4.Graduate University of Chinese Academy of SciencesBeijingChina

Personalised recommendations