Search:
Computing and Library Services - delivering an inspiring information environment

Efficient Discovery of Spatial Co-Location Patterns Using the iCPI-tree

Wang, Lizhen, Bao, Yuzhen and Lu, Joan (2009) Efficient Discovery of Spatial Co-Location Patterns Using the iCPI-tree. The Open Information Systems Journal, 3 (2). pp. 69-80. ISSN 1874-1339

Metadata only available from this repository.

Abstract

With the rapid growth and extensive applications of the spatial dataset, it's 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. It's 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 identifying the table instances of co-location patterns. The essence of co-location patterns discovery and four 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----iCPI-tree (Improved Co-location Pattern Instance Tree), is proposed. The iCPI-tree is an improved version of the CPI-tree which materializes spatial neighbor relationships in order to accelerate the process of identifying co-location instances. This paper proves the correctness and completeness of the new approach. Finally, an experimental evaluations using synthetic and real world datasets show that the algorithm is computationally more efficient.

Item Type: Article
Subjects: 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 > Informatics Research Group
School of Computing and Engineering > Informatics Research Group > Software Engineering Research Group
School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
Related URLs:
Depositing User: Cherry Edmunds
Date Deposited: 01 Sep 2010 14:17
Last Modified: 09 Dec 2010 12:34
URI: http://eprints.hud.ac.uk/id/eprint/8427

Item control for Repository Staff only:

View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©