A new method based on association rules mining and geo-filter for mining spatial association knowledge |
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| Authors: | Yaolin Liu Peng Xie Qingsong He Xiang Zhao Xiaojian Wei Ronghui Tan |
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| Institution: | 1.School of Resource and Environment Science,Wuhan University,Wuhan,China;2.Key Laboratory of Geographic Information System, Ministry of Education,Wuhan University,Wuhan,China;3.Faculty of Geomatics,East China Institute of Technology,Nanchang,China;4.The College of Management and Economics,Tianjin University,Tianjin,China |
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| 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. |
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