Self-Organising Maps

dc.contributor.editor	Agarwal, Pragya
dc.contributor.editor	ANDRÉ SKUPIN
dc.date.accessioned	2019-03-04T14:07:24Z
dc.date.accessioned	2023-07-20T14:35:00Z
dc.date.available	2019-03-04T14:07:24Z
dc.date.available	2023-07-20T14:35:00Z
dc.date.issued	2008
dc.identifier.isbn	978-0-470-02167-5
dc.identifier.uri	http://10.215.13.25/handle/123456789/50206
dc.description	This 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.language	en	en_US
dc.language.iso	en	en_US
dc.publisher	John Wiley & Sons, Ltd	en_US
dc.subject	Geographic information systems—Mathematical models	en_US
dc.title	Self-Organising Maps	en_US
dc.title.alternative	Applications in Geographic Information Science	en_US
dc.type	Book	en_US