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A new join-less approach for co-location pattern mining

Wang, L, Bao, Y, Lu, J and Yip, YJ 2008, A new join-less approach for co-location pattern mining , in: 8th IEEE International Conference on Computer and Information Technology, 2008, Sydney, NSW.

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Abstract

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: Conference or Workshop Item (Paper)
Additional Information: Paper from the 8th IEEE International Conference on Computer and Information Technology, 2008. CIT 2008. Sydney, NSW, 2008
Themes: Media, Digital Technology and the Creative Economy
Schools: Colleges and Schools > College of Science & Technology
Strategic Leadership Team
Publisher: IEEE
Refereed: No
Depositing User: Users 29196 not found.
Date Deposited: 14 Aug 2012 14:07
Last Modified: 20 Aug 2013 18:30
URI: http://usir.salford.ac.uk/id/eprint/23120

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