Descriptive Modelling Pattern Discovery by Sharma Lokesh (6 results)
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Taschenbuch. Condition: Neu. Descriptive Modelling and Pattern Discovery in Spatial Data Mining | Regionalisation and Association Rule Mining | Lokesh Kumar Sharma | Taschenbuch | 116 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846592151 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpe…n 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.
Language: English
Published by LAP LAMBERT Academic Publishing Nov 2011, 2011
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma Lokesh KumarDr. Sharma received his Ph. D. degree from Pt. Ravishankar Shukla University, Raipur-India. Dr. Sharma is a DAAD Fellow and Former member of Knowledge Discovery Department, Fraunhofer… IAIS St. Augustin Germany. He .
Language: English
Published by LAP LAMBERT Academic Publishing Nov 2011, 2011
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 spa…tial 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 spat…ial 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.
