Please use this identifier to cite or link to this item: 192.168.6.56/handle/123456789/50206
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dc.contributor.editorAgarwal, Pragya-
dc.contributor.editorANDRÉ SKUPIN-
dc.date.accessioned2019-03-04T14:07:24Z-
dc.date.available2019-03-04T14:07:24Z-
dc.date.issued2008-
dc.identifier.isbn978-0-470-02167-5-
dc.identifier.urihttp://10.6.20.12:80/handle/123456789/50206-
dc.descriptionThis edited volume aims to demonstrate that there is indeed something special about this method, something that makes it curiously attractive to diverse and sometimes conflicting interests and approaches in GIScience. Those interested in clustering and classification will recognize in it elements of k-means clustering, but with an explicit representation of topological relationships between clusters. Anyone accustomed to dealing with ndimensional data through a transformation and reduction of variables, as in principal components analysis (PCA) or multidimensional scaling, will tend to interpret the SOM method in that light-
dc.languageenen_US
dc.language.isoenen_US
dc.publisherJohn Wiley & Sons, Ltden_US
dc.subjectGeographic information systems—Mathematical modelsen_US
dc.titleSelf-Organising Mapsen_US
dc.title.alternativeApplications in Geographic Information Scienceen_US
dc.typeBooken_US
Appears in Collections:Geography

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