中国地理科学 ›› 2017, Vol. 27 ›› Issue (3): 389-401.doi: 10.1007/s11769-017-0873-y

• 论文 • 上一篇    下一篇

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

LIU Yaolin1,2, XIE Peng1, HE Qingsong1, ZHAO Xiang1, WEI Xiaojian3, TAN Ronghui4   

  1. 1. School of Resource and Environment Science, Wuhan University, Wuhan 430079, China;
    2. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China;
    3. Faculty of Geomatics, East China Institute of Technology, Nanchang 330013, China;
    4. The College of Management and Economics, Tianjin University, Tianjin 300072, China
  • 收稿日期:2016-07-12 修回日期:2016-11-08 出版日期:2017-06-27 发布日期:2017-05-09
  • 通讯作者: XIE Peng. E-mail: xiepenggis@163.com E-mail:xiepenggis@163.com
  • 基金资助:

    Under the auspices of Special Fund of Ministry of Land and Resources of China in Public Interest (No. 201511001)

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

LIU Yaolin1,2, XIE Peng1, HE Qingsong1, ZHAO Xiang1, WEI Xiaojian3, TAN Ronghui4   

  1. 1. School of Resource and Environment Science, Wuhan University, Wuhan 430079, China;
    2. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China;
    3. Faculty of Geomatics, East China Institute of Technology, Nanchang 330013, China;
    4. The College of Management and Economics, Tianjin University, Tianjin 300072, China
  • Received:2016-07-12 Revised:2016-11-08 Online:2017-06-27 Published:2017-05-09
  • Contact: XIE Peng. E-mail: xiepenggis@163.com E-mail:xiepenggis@163.com
  • Supported by:

    Under the auspices of Special Fund of Ministry of Land and Resources of China in Public Interest (No. 201511001)

摘要:

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining (GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%-70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

关键词: data mining, association rules, rules spatial visualization, driving factors analysis, land use change

Abstract:

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining (GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%-70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

Key words: data mining, association rules, rules spatial visualization, driving factors analysis, land use change