2011, ISBN: 9783846592151

[ED: Taschenbuch / Paperback], [PU: LAP Lambert Academic Publishing], The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of in… Mehr…

[ED: Taschenbuch / Paperback], [PU: LAP Lambert Academic Publishing], The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other significant mode not explicitly stored in the spatial databases. Knowledge discovery in large spatial database is important for the extraction of implicit knowledge. Spatial relations or other patterns are not explicitly stored in spatial database. Traditional Data mining techniques are not efficient and effective to mine the spatial data due to its unique features such as spatial dependency, heterogeneity, spatially aggregated data etc. Thus, new and efficient mining methods are needed to discover knowledge from large spatial databases. A descriptive modeling technique for georeferenced data is discussed and it is also used to solve the regionalization problem. Multi Level Multi Dimensional is an important aspect for Spatial Data. The Multi Level Multi Dimensional pattern discovery on spatial data is presented here. Mining the trajectory data or mobility data is an emerging area of research. The Trajectory data classifier which is based on the Nearest Neighbor is introduced., DE, [SC: 0.00], Neuware, gewerbliches Angebot, H: 219mm, B: 149mm, T: 7mm, 116, [GW: 171g], Selbstabholung und Barzahlung, PayPal, Offene Rechnung, Banküberweisung, Internationaler Versand<

ISBN: 3846592153

[EAN: 9783846592151], Neubuch, [PU: LAP Lambert Academic Publishing], DATA MINING, Druck auf Anfrage Neuware - The explosive growth of spatial data and the widespread use of spatial datab… Mehr…

[EAN: 9783846592151], Neubuch, [PU: LAP Lambert Academic Publishing], DATA MINING, Druck auf Anfrage Neuware - The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other significant mode not explicitly stored in the spatial databases. Knowledge discovery in large spatial database is important for the extraction of implicit knowledge. Spatial relations or other patterns are not explicitly stored in spatial database. Traditional Data mining techniques are not efficient and effective to mine the spatial data due to its unique features such as spatial dependency, heterogeneity, spatially aggregated data etc. Thus, new and efficient mining methods are needed to discover knowledge from large spatial databases. A descriptive modeling technique for georeferenced data is discussed and it is also used to solve the regionalization problem. Multi Level Multi Dimensional is an important aspect for Spatial Data. The Multi Level Multi Dimensional pattern discovery on spatial data is presented here. Mining the trajectory data or mobility data is an emerging area of research. The Trajectory data classifier which is based on the Nearest Neighbor is introduced. 116 pp. Englisch, Books<

2011, ISBN: 9783846592151

The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other … Mehr…

The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other significant mode not explicitly stored in the spatial databases. Knowledge discovery in large spatial database is important for the extraction of implicit knowledge. Spatial relations or other patterns are not explicitly stored in spatial database. Traditional Data mining techniques are not efficient and effective to mine the spatial data due to its unique features such as spatial dependency, heterogeneity, spatially aggregated data etc. Thus, new and efficient mining methods are needed to discover knowledge from large spatial databases. A descriptive modeling technique for georeferenced data is discussed and it is also used to solve the regionalization problem. Multi Level Multi Dimensional is an important aspect for Spatial Data. The Multi Level Multi Dimensional pattern discovery on spatial data is presented here. Mining the trajectory data or mobility data is an emerging area of research. The Trajectory data classifier which is based on the Nearest Neighbor is introduced. Buch (fremdspr.) Lokesh Kumar Sharma Taschenbuch, LAP LAMBERT Academic Publishing, 01.11.2011, LAP LAMBERT Academic Publishing, 2011<

ISBN: 9783846592151

The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other … Mehr…

The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other significant mode not explicitly stored in the spatial databases. Knowledge discovery in large spatial database is important for the extraction of implicit knowledge. Spatial relations or other patterns are not explicitly stored in spatial database. Traditional Data mining techniques are not efficient and effective to mine the spatial data due to its unique features such as spatial dependency, heterogeneity, spatially aggregated data etc. Thus, new and efficient mining methods are needed to discover knowledge from large spatial databases. A descriptive modeling technique for georeferenced data is discussed and it is also used to solve the regionalization problem. Multi Level Multi Dimensional is an important aspect for Spatial Data. The Multi Level Multi Dimensional pattern discovery on spatial data is presented here. Mining the trajectory data or mobility data is an emerging area of research. The Trajectory data classifier which is based on the Nearest Neighbor is introduced. Bücher, Hörbücher & Kalender / Bücher / Sachbuch / Computer & IT<

2011, ISBN: 3846592153

Kartoniert / Broschiert, mit Schutzumschlag 11, [PU:LAP LAMBERT Academic Publishing]