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Spatial clustering method considering spatial distribution feature in the attribute domain

  • role： First author第一作者
  • Email：Chu_Je@163.com
  • Introduction：朱杰(1989—　)，男，博士研究生，研究方向为空间数据表达与挖掘。E-mail：Chu_Je@163.com
Jie ZHU,  
  • role： Corresponding author通信作者
  • Email：sunyizhong_cz@163.com
  • Introduction：孙毅中(1957—　)，男，教授，研究方向为城市空间数据挖掘。E-mail：sunyizhong_cz@163.com
Yizhong SUN*,  
  • No information about the author is available
Jilong LI

resumen

Spatial clustering is important for spatial data mining and spatial analysis. Spatial objects in the same cluster should be similar in the spatial and attribute domains. Tendency and heterogeneity are important characteristics of geographic phenomena. Currently, most spatial clustering algorithms only consider either tendency or heterogeneity, failing to obtain satisfied clustering results. To overcome these limitations, a spatial clustering method based on graph theory and information entropy is developed in this work. The proposed algorithm involves two main steps: construct spatial proximity relationships and cluster spatial objects with similar attributes. Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships among objects. To obtain satisfactory results in spatial clustering with attribute similarity, the information entropy is introduced to overcome the defects of similarity measure with binary relation, which can reflect the clustering tendency of geographical phenomena. Furthermore, a local parameter measurement method based on the principle of “equal probability maximum entropy” is designed to adapt to the local change information of attribute distribution. The performance of the proposed algorithm was evaluated experimentally by comparing the leading state-of-the-art alternatives: DDBSC and multi-constraint algorithms. Results showed that our method outperformed the two other algorithms as attributes are unevenly distributed in space. The sensitivity analysis of these algorithms showed that our method was the least sensitive to outliers. The effectiveness and practicability of the proposed algorithm were validated using simulated and real spatial datasets. Two experiments were performed to illustrate the three advantages of our algorithm: (1) It can reflect the tendency of the entity attribute in the spatial distribution. (2) It can meet the requirement that attributes are unevenly distributed in space. (3) It can discover clusters with arbitrary shape and is robust to outliers.

palabra clave

spatial clustering;Delaunay triangulation;information entropy;tendency;heterogeneity

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