Descriptive Modelling and Pattern Discovery in Spatial Data Mining: Regionalisation and Association Rule Mining - Softcover

Sharma, Lokesh Kumar

 
9783846592151: Descriptive Modelling and Pattern Discovery in Spatial Data Mining: Regionalisation and Association Rule Mining
Softcover
ISBN 10: 3846592153 ISBN 13: 9783846592151
Verlag: LAP LAMBERT Academic Publishing, 2011

Inhaltsangabe

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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Reseña del editor

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.

Biografía del autor

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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Verlag
LAP LAMBERT Academic Publishing
Erscheinungsdatum
2011
Sprache
Englisch
ISBN 10 
3846592153
ISBN 13 
9783846592151
Einband
Tapa blanda
Anzahl der Seiten
116
Kontakt zum Hersteller
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Verantwortliche Person
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