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Descriptive Modelling and Pattern Discovery in Spatial Data Mining: Regionalisation and Association Rule Mining - Couverture souple
Synopsis
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.
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Présentation de l'éditeur
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.
Biographie de l'auteur
Dr. 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 is working as Head Department of Computer Science and Engineering at Rungta College of Engineering and Technology, Bhilai (CG) India.
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- ÉditeurLAP LAMBERT Academic Publishing
- Date d'édition2011
- ISBN 10 3846592153
- ISBN 13 9783846592151
- ReliureBroché
- Langueanglais
- Nombre de pages116
- Coordonnées du fabricantnon disponible
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Descriptive Modelling and Pattern Discovery in Spatial Data Mining : Regionalisation and Association Rule Mining
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3846592153
ISBN 13 : 9783846592151
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Etat : Hervorragend. Zustand: Hervorragend | Seiten: 116 | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 11605645/1
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Descriptive Modelling and Pattern Discovery in Spatial Data Mining
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3846592153
ISBN 13 : 9783846592151
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Couverture souple
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Etat : 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 . N° de réf. du vendeur 5501813
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Descriptive Modelling and Pattern Discovery in Spatial Data Mining : Regionalisation and Association Rule Mining
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3846592153
ISBN 13 : 9783846592151
Neuf
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Taschenbuch. Etat : 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 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. N° de réf. du vendeur 9783846592151
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Descriptive Modelling and Pattern Discovery in Spatial Data Mining
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LAP LAMBERT Academic Publishing Nov 2011, 2011
ISBN 10 : 3846592153
ISBN 13 : 9783846592151
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783846592151
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Descriptive Modelling and Pattern Discovery in Spatial Data Mining
Edité par
LAP LAMBERT Academic Publishing Nov 2011, 2011
ISBN 10 : 3846592153
ISBN 13 : 9783846592151
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 116 pp. Englisch. N° de réf. du vendeur 9783846592151
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Descriptive Modelling and Pattern Discovery in Spatial Data Mining: Regionalisation and Association Rule Mining
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3846592153
ISBN 13 : 9783846592151
Neuf
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Paperback. Etat : Brand New. 116 pages. 8.58x5.83x0.31 inches. In Stock. N° de réf. du vendeur 3846592153
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