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dc.contributor.authorWoodford, Brendon Jen_NZ
dc.date.available2011-04-07T03:03:10Z
dc.date.copyright2003-12en_NZ
dc.identifier.citationWoodford, B. J. (2003, December). Spatial data mining: where to from here? Presented at the 15th Annual Colloquium of the Spatial Information Research Centre (SIRC 2003: Land, Place and Space).en
dc.identifier.urihttp://hdl.handle.net/10523/810
dc.descriptionOnly the abstract and references were published in the proceedings. There is no full text.en_NZ
dc.description.abstractThe field of spatial data mining (Chawla, Shekhar,Wu & Ozesmi 2001), has been influenced by many other disciplines such as neural networks (Rumelhart, Hinton & Williams 1986), machine learning (Mitchell 1997), fuzzy systems (Zadeh 1965), and statistics (Sammon 1969). Recently other methods and techniques have been developed that offer some advantages over the conventional methods that have been applied in the past. For example the Support Vector Machine (SVM) (Cortes & Vapnik 1995) is one technique that can identify clusters where it may be difficult to easily separate different regions and new learning systems have now been developed that address the problem of local versus global learning models for spatial data analysis (Gilardi 2002). In this presentation we review the methods and techniques that have been previously employed for the purpose of spatial data mining and also introduce some new technologies that could be applied to this task.en_NZ
dc.format.mimetypeapplication/pdf
dc.relation.urihttp://www.business.otago.ac.nz/SIRC05/conferences/2003/16_Woodford.pdfen_NZ
dc.subjectclusteringen_NZ
dc.subjectsimilarity metricsen_NZ
dc.subjectmachine learningen_NZ
dc.subjectfuzzy systemsen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleSpatial data mining: where to from here?en_NZ
dc.typeConference or Workshop Item (Oral presentation)en_NZ
dc.description.versionPublisheden_NZ
otago.date.accession2005-11-30en_NZ
otago.relation.pages95en_NZ
otago.openaccessOpen
dc.identifier.eprints107en_NZ
dc.description.refereedNon Peer Revieweden_NZ
otago.school.eprintsSpatial Information Research Centreen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.referencesChawla, S., Shekhar, S., Wu, W. & Ozesmi, U. (2001). “Modeling Spatial Dependencies for Mining Geospatial Data: An Introduction” In H. J. Miller & J. Han (eds), Geographic Data Mining and Knowledge Discovery. Taylor and Francis. Cortes, C. & Vapnik, V. (1995). “Support-Vector Networks” Machine Learning. 20(3): 273–297. Gilardi, N. (2002). “Local Machine Learning Models for Spatial Data Analysis” Geographical Information and Decision Analysis. 4(1): 11–28. Mitchell, M. T. (1997). Machine Learning. MacGraw-Hill. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Parallel Distributed Processing, Vols 1 and 2. The MIT Press: Cambridge, MA. Sammon, J. W. (1969). “A Nonlinear Mapping for Data Structure Analysis” IEEE Transactions on Computers. 18: 401–409. Zadeh, L. (1965). “Fuzzy Sets” Information and Control. 8: 338–353.en_NZ
otago.event.dates1-2 December 2003en_NZ
otago.event.placeDunedin, New Zealanden_NZ
otago.event.typeconferenceen_NZ
otago.event.title15th Annual Colloquium of the Spatial Information Research Centre (SIRC 2003: Land, Place and Space)en_NZ
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