计算机工程与应用 ›› 2017, Vol. 53 ›› Issue (6): 22-28.DOI: 10.3778/j.issn.1002-8331.1609-0072

• 热点与综述 • 上一篇    下一篇

基于趋势函数的空间数据聚类方法

李建勋1,申静静1,李维乾2,王婉琳1   

  1. 1.西安理工大学 经济与管理学院,西安 710054
    2.西安工程大学 计算机科学学院,西安 710048
  • 出版日期:2017-03-15 发布日期:2017-05-11

Cluster method for spatial data based on trend function

LI Jianxun1, SHEN Jingjing1, LI Weiqian2, WANG Wanlin1   

  1. 1.College of Economics and Management, Xi’an University of Technology, Xi’an 710054, China
    2.College of Computer Science, Xi’an Polytechnic University, Xi’an 710048, China
  • Online:2017-03-15 Published:2017-05-11

摘要: 针对空间数据聚类不重视属性数据利用的问题,在探讨空间属性数据趋向性的基础上,构建刻画属性值随空间位置变化的趋势函数,形成了借鉴于变异函数的二阶模型,并据此建立一个集成空间距离和属性差异的相似度函数,探讨了满足平稳假设和角度容限处理方案,形成了一个以趋势函数为核心的聚类模型K-Trend。实验表明:该算法聚类结果质量高,不受样本影响,且耗时适中,进一步提高了空间数据聚类的实用性。

关键词: 趋势函数, 空间数据, 聚类

Abstract: According to the neglect problem of the importance of attribute data while spatial data clustering and the tendency exploration of spatial feature data, a trend function is established for describing the attribute value change with spatial location. Then, second-order model is constructed by referencing to the variation function. In view of the above, a similarity function integrated spatial distance and attribute difference is built. A treatment scheme regarding of angle tolerance is discussed under stationary hypothesis. Finally, a cluster model named K-Trend is set up with taking trend function as a core. The results show that the K-Trend cluster method has high quality, is seldom affected by sample size, and has moderate time-consuming. All of these features improve the practicability of spatial data cluster.

Key words: trend function, spatial data, cluster