Mining spatial association rules with multi-relational approach

Authors:
Min Qian;Li-Jie Pu;Rong Fu;Ming Zhu
Affiliations:
School of Geographic and Oceangrahphic Sciences, Nanjing University, Nanjing, China and School of Resource and Environment, Guizhou University, Guiyang, China;School of Geographic and Oceangrahphic Sciences, Nanjing University, Nanjing, China;School of Geographic and Oceangrahphic Sciences, Nanjing University, Nanjing, China;School of Geographic and Oceangrahphic Sciences, Nanjing University, Nanjing, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

Working on spatial data models for geographic phenomena have always been viewed from a spatial context and emphasizing spatial change. But the problem is that spatial relationships are embedded in space, unknown a priori. To achieve such issue, spatial association rules mining techniques are needed. In this paper, we propose a multi-relational mining method to deal with it. We use a non-parametric way by using Vironoi-diagram based neighborhood, classification method is implemented to pre-process the rules condition, association rules are pre-defined, and a close Apriori-base algorithm is proposed to cope with it. Then the framework is evaluated by the real-world dataset, and some thoughtful association rules are given.