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Article: Knowledge discovery for geographical cellular automata

TitleKnowledge discovery for geographical cellular automata
Authors
KeywordsCellular automata
Geographical information systems
Geographical simulation
Knowledge discovery
Issue Date2005
PublisherScience in China Press. The Journal's web site is located at http://jdxg.chinajournal.net.cn/
Citation
Science In China, Series D: Earth Sciences, 2005, v. 48 n. 10, p. 1758-1767 How to Cite?
AbstractThis paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. Copyright by Science in China Press 2005.
Persistent Identifierhttp://hdl.handle.net/10722/89893
ISSN
2011 Impact Factor: 1.588
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Xen_HK
dc.contributor.authorYeh, AGOen_HK
dc.date.accessioned2010-09-06T10:03:05Z-
dc.date.available2010-09-06T10:03:05Z-
dc.date.issued2005en_HK
dc.identifier.citationScience In China, Series D: Earth Sciences, 2005, v. 48 n. 10, p. 1758-1767en_HK
dc.identifier.issn1006-9313en_HK
dc.identifier.urihttp://hdl.handle.net/10722/89893-
dc.description.abstractThis paper proposes a new method for geographical simulation by applying data mining techniques to cellular automata. CA has strong capabilities in simulating complex systems. The core of CA is how to define transition rules. There are no good methods for defining these transition rules. They are usually defined by using heuristic methods and thus subject to uncertainties. Mathematical equations are used to represent transition rules implicitly and have limitations in capturing complex relationships. This paper demonstrates that the explicit transition rules of CA can be automatically reconstructed through the rule induction procedure of data mining. The proposed method can reduce the influences of individual knowledge and preferences in defining transition rules and generate more reliable simulation results. It can efficiently discover knowledge from a vast volume of spatial data. Copyright by Science in China Press 2005.en_HK
dc.languageengen_HK
dc.publisherScience in China Press. The Journal's web site is located at http://jdxg.chinajournal.net.cn/en_HK
dc.relation.ispartofScience in China, Series D: Earth Sciencesen_HK
dc.subjectCellular automataen_HK
dc.subjectGeographical information systemsen_HK
dc.subjectGeographical simulationen_HK
dc.subjectKnowledge discoveryen_HK
dc.titleKnowledge discovery for geographical cellular automataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0036-8237&volume=48&issue=10&spage=1758&epage=1767&date=2005&atitle=Knowledge+Discovery+for+Geographical+Cellular+Automataen_HK
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_HK
dc.identifier.authorityYeh, AGO=rp01033en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.scopuseid_2-s2.0-33845775230en_HK
dc.identifier.hkuros117643en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33845775230&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume48en_HK
dc.identifier.issue10en_HK
dc.identifier.spage1758en_HK
dc.identifier.epage1767en_HK
dc.identifier.isiWOS:000233344000019-
dc.publisher.placeChinaen_HK
dc.identifier.scopusauthoridLi, X=34872691500en_HK
dc.identifier.scopusauthoridYeh, AGO=7103069369en_HK

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