Descriptive Modelling and Pattern Discovery in Spatial Data Mining

Name: Descriptive Modelling and Pattern Discovery in Spatial Data Mining
Brand: LAP Lambert Academic Publishing
SKU: 9783846592151
Price: 179.91 PLN
Availability: InStock

Regionalisation and Association Rule Mining

Autor: Lokesh Kumar Sharma

Język:

Oprawa: Miękka

Wydawca: LAP Lambert Academic Publishing

Dostępność: Dostępna u dostawcy

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The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the...

Informacje o książce

Autor

Lokesh Kumar Sharma

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2011

strony

116

EAN

9783846592151

Enbook ID

07089574

Wydawca

LAP Lambert Academic Publishing

Waga

177

Wymiary

149 x 219 x 7

Pełny opis

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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Descriptive Modelling and Pattern Discovery in Spatial Data Mining

Regionalisation and Association Rule Mining

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