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Extracting spatial association rules from spatial transactions
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Source Geographic Information Systems archive
Proceedings of the 13th annual ACM international workshop on Geographic information systems table of contents
Bremen, Germany
SESSION: Data integration and data mining table of contents
Pages: 79 - 86  
Year of Publication: 2005
ISBN:1-59593-146-5
Authors
Salvatore Rinzivillo  Università di Pisa, Pisa, Italy
Franco Turini  Università di Pisa, Pisa, Italy
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 11,   Downloads (12 Months): 120,   Citation Count: 1
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Ferenc Bodon. A fast apriori implementation. In FIMI, 2003.
 
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M. J. Egenhofer and R. D. Franzosa. Point-set topological spatial relations. International Journal on Geographical Information systems, 5(2):161--174, 1991.
 
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Jiawei Han, Krzysztof Koperski, and Nebojsa Stefanovic. An efficient two-step method for classification of spatial data, 1999.
 
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D. Malerba, F. Esposito, and F.A. Lisi. Mining spatial association rules in census data. In Proc. of the Joint Conf. on "New Techniques and Technologies for Statistcs" and "Exchange of Technology and Know-how", 2001.
 
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Donato Malerba and Francesca A. Lisi. An ILP method for spatial association rule mining, August 23 2001.
 
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Salvatore Orlando, Claudio Lucchese, P. Palmerini, Raffaele Perego, and Fabrizio Silvestri. kDCI: a multi-strategy algorithm for mining frequent sets. In Proceedings of the Workshop on Frequent Itemset Mining Implementations (FIMI'03), in conjunction with ICDM'03, November 2003.
 
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Collaborative Colleagues:
Salvatore Rinzivillo: colleagues
Franco Turini: colleagues