Efficient discovery of multilevel spatial association rules using partitions

Authors:
Lizhen Wang;Kunqing Xie;Tao Chen;Xiuli Ma
Affiliations:
Department of Computer Science and Engineering, School of Information, Yunnan University, Kunming 650091, People's Republic of China;National Laboratory on Machine Perception, Center for Information Science, Peking University, Beijing, 100871, People's Republic of China;Department of Computer Science and Engineering, School of Information, Yunnan University, Kunming 650091, People's Republic of China;National Laboratory on Machine Perception, Center for Information Science, Peking University, Beijing, 100871, People's Republic of China
Venue:
Information and Software Technology
Year:
2005

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Abstract

Spatial data mining has been identified as an important task for understanding and use of spatial data- and knowledge-bases. In this paper, we present a new approach to discover strong multilevel spatial association rules in spatial databases based on partitioning the set of rows with respect to the spatial relations denoted as relation table R. Meanwhile, the introduction of the equivalence partition tree makes the discovery of multilevel spatial association rules easy and efficient. Experiments show that the new algorithm is efficient.