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Extracting spatial association rules from spatial transactions

Published: 04 November 2005 Publication History

Abstract

Georeferenced information is growing every day, and geographical information systems are becoming crucial in many decision processes. As a consequence, extracting knowledge from GIS's may have an important impact. The paper presents a general approach for extracting sets of spatial transactions from GIS's, and for applying data mining algorithms to them. As a basic example of the process we present the extraction of spatial association rules from georeferenced data.

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cover image ACM Conferences
GIS '05: Proceedings of the 13th annual ACM international workshop on Geographic information systems
November 2005
306 pages
ISBN:1595931465
DOI:10.1145/1097064
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 04 November 2005

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  1. spatial association rules
  2. spatial transactions

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

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  • (2023)Efficiently mining maximal l-reachability co-location patterns from spatial data setsIntelligent Data Analysis10.3233/IDA-21651527:1(269-295)Online publication date: 30-Jan-2023
  • (2022)Mining Non-Redundant Co-Location PatternsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308262833:11(6613-6626)Online publication date: Nov-2022
  • (2022)Knowledge-Based Interactive Postmining of User-Preferred Co-Location Patterns Using OntologiesIEEE Transactions on Cybernetics10.1109/TCYB.2021.305492352:9(9467-9480)Online publication date: Sep-2022
  • (2022)Discovery of Spatial Association Rules from Fuzzy Spatial DataConceptual Modeling10.1007/978-3-031-17995-2_13(179-193)Online publication date: 10-Oct-2022
  • (2014)A framework of spatial co-location pattern mining for ubiquitous GISMultimedia Tools and Applications10.1007/s11042-012-1007-271:1(199-218)Online publication date: 1-Jul-2014
  • (2012)A Constraint Neighborhood Based Approach for Co-location Pattern MiningProceedings of the 2012 Fourth International Conference on Knowledge and Systems Engineering10.1109/KSE.2012.16(128-135)Online publication date: 17-Aug-2012
  • (2011)Who/Where Are My New Customers?Emerging Intelligent Technologies in Industry10.1007/978-3-642-22732-5_25(307-317)Online publication date: 2011
  • (2011)Maximal Cliques Generating Algorithm for Spatial Co-location Pattern MiningSecure and Trust Computing, Data Management and Applications10.1007/978-3-642-22339-6_29(241-250)Online publication date: 2011
  • (2008)Knowledge Discovery from Geographical DataMobility, Data Mining and Privacy10.1007/978-3-540-75177-9_10(243-265)Online publication date: 2008

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