Paper Info
Title
Discovery of generalized spatial association rules
Abstract
Spatial association rule mining is an important technique of spatial data mining and business intelligence. Nevertheless, traditional spatial association rule mining approaches have a significant limitation that they cannot effectively involve and exploit non-spatial information. As a result, many interesting rules mixing spatial and non-spatial information which provide extra insights and tell the hidden patterns cannot be found. In this paper, we propose a novel approach to discover the Generalized Spatial Association Rules (GSAR), which are capable of expressing richer information including not only spatial, but also non-spatial and taxonomy information of spatial objects. Meanwhile, the additional computation introduced only costs linear time complexity. A case study on a real crime dataset shows that using the proposed approach, many interesting and meaningful crime patterns can be discovered. However, traditional approaches cannot find such patterns at all.
Year
DOI
Venue
2012
10.1109/SOLI.2012.6273505
Service Operations and Logistics, and Informatics
Keywords
DocType
ISBN
competitive intelligence,computational complexity,criminal law,data mining,gsar,business intelligence,crime patterns,generalized spatial association rules discovery,hidden patterns,linear time complexity,nonspatial information,real crime dataset,richer information,spatial association rule mining,spatial data mining,spatial objects,taxonomy information,taxonomy
Conference
978-1-4673-2400-7
Citations 
PageRank 
References 
2
0.77
7
Authors
7
Name
Order
Citations
PageRank
Weishan Dong118713.44
li li220.77
changjin zhou320.77
yu wang420.77
min li521.11
Chunhua Tian67416.63
SUN Wei724726.63