Wang, Lizhen, Bao, Yuzhen, Lu, Joan and Yip, Yau Jim (2008) A new join-less approach for co-location pattern mining. In: 2008 8th IEEE International Conference on Computer and Information Technology. IEEE, pp. 197-202. ISBN 9781424423576Metadata only available from this repository.
With the rapid growth and extensive applications of the spatial dataset, itpsilas getting more important to solve how to find spatial knowledge automatically from spatial datasets. Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Itpsilas difficult to discovery co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to generating the table instances of co-location patterns. The essence of co-location patterns discovery and three kinds of co-location patterns mining algorithms proposed in recent years are analyzed, and a new join-less approach for co-location patterns mining, which based on a data structure - CPI-tree (Co-location Pattern Instance Tree), is proposed. The CPI-tree materializes spatial neighbor relationships. All co-location table instances can be generated quickly with a CPI-tree. This paper proves the correctness and completeness of the new approach. Finally, an experimental evaluation using synthetic datasets and a real world dataset shows that the algorithm is computationally more efficient than the join-less algorithm
|Item Type:||Book Chapter|
|Additional Information:||Paper from the 8th IEEE International Conference on Computer and Information Technology, 2008. CIT 2008. Sydney, NSW, 2008|
|Subjects:||T Technology > T Technology (General)|
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
|Schools:||School of Computing and Engineering|
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
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School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
|Depositing User:||Briony Heyhoe|
|Date Deposited:||07 Jan 2009 10:49|
|Last Modified:||20 Jun 2013 12:54|
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