Man-Land Relationship

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

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  • 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. School of Resources and Environment Science, Wuhan University, Wuhan 430079, China;
    3. Institute of Policy and Management, CAS, Beijing 100080, China;
    4. Graduate University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2010-02-06

  Revised date: 2010-04-16

  Online published: 2010-10-15

Supported by

National Natural Science Foundation of China, No.40971102; Knowledge Innovation Project of the Chinese Academy of Sciences, No. KZCX2-YW-322; Special Grant for Postgraduates’ Scientific Innovation and Social Practice in 2008

Abstract

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.

Cite this article

DENG Yu, LIU Shenghe, ZHANG Wenting, WANG Li, WANG Jianghao . General multidimensional cloud model and its application on spatial clustering in Zhanjiang, Guangdong[J]. Journal of Geographical Sciences, 2010 , 20(5) : 787 -798 . DOI: 10.1007/s11442-010-0811-8

References


[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.

[2] Berkhin P, 2000. Survey of clustering data mining techniques. Accrue Software.

[3] Bezdek J C, 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press.

[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)

[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)

[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.

[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)

[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)

[9] Karypic G., Han E H, 1999. CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. Computer, 32: 68–75.

[10] Kaufman L, Rousseeuw P J, 1990. Finding groups in data: An introduction to cluster analysis. Wiley & Sons. Li Deyi, 2000. Uncertainty in knowledge representation. Engineering Science, 2(10): 73–76. (in Chinese)

[11] 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.

[12] 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.

[13] Li Deyi, Liu Changyu, 2004. Study on the universality of the normal cloud model. Engineering Science, 6(8): 29–33. (in Chinese)

[14] Li Deyi, Liu Changyu, Du Yu et al., 2004. Artificial intelligence with uncertainty. Journal of Software, 15(11): 1583–1594. (in Chinese)

[15] Li Deyi, Meng Haijun, Shi Xuemei, 1995. Membership cloud and membership cloud generator. Computer Research and Development, 32(6): 15–20. (in Chinese)

[16] Li Xinyun, Zheng Xinqi, 2004. On spatial clustering of combination of coordinate and attribute. Geography and Geoinformation Science, 3: 38–40. (in Chinese)

[17] Liu Changyu, 2005. Some statistical analysis of the normal cloud model. Information and Control, 2005, 4: 237–243. (in Chinese)

[18] Liu Changyu, Dai Xiaojun, Li Deyi, 2004. A new algorithm of backward cloud. Journal of System Simulation, 16(11): 2417–2420. (in Chinese)

[19] Liu Guihua, 2007. Research on association rules based on cloud mode
[D]. Jinan: Shandong Normal University. Macqueen J, 1967. Some methods for classification and analysis of multivariate observations. California: Berkeley.

[20] Qin Kun, Li Deyi, Xu Kai, 2006. Image segmentation based on cloud model. Survey and Mapping Information Engineering, 31(5): 3–6. (in Chinese)

[21] 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.

[22] Tang Yijian, 1986. Regional river water quality fuzzy cluster analysis. Acta Geographica Sinica, 41(3): 234–241. (in Chinese)

[23] Wang H J, 2007a. Spatial clustering method based on cloud model and data field. Advances in Computation and Intelligence, 4683: 420–427.

[24] Wang H J, 2007b. Spatial clustering method based on cloud model. Fuzzy Systems and Knowledge Discovery, 2: 272–276.

[25] 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.

[26] 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)

[27] 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.

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