Adaptive Ttwo-phase spatial association rules mining method

 

Autores
Lee, Chin-Feng; Chen, Mei-Hsiu
Tipo de recurso
artículo
Estado
Versión publicada
Año de publicación
2006
País
Argentina
Institución
Universidad Nacional de La Plata
Repositorio
SEDICI (UNLP)
Descripción
Since huge amounts of spatial data can be easily collected from various applications, ranging from remote sensing technology to geographical information system, the extraction and comprehension of spatial knowledge is a more and more important task. Many excellent studies on Remote Sensed Image (RSI) have been conducted for potential relationships of crop yield. However, most of them suffer from the performance problem because their techniques for mining association rules are based on Apriori algorithm. In this paper, two efficient algorithms, two-phase spatial association rules mining and adaptive two-phase spatial association rules mining, are proposed for address the above problem. Both methods primarily conduct two phase algorithms by creating Histogram Generators for fast generating coarse-grained spatial association rules, and further mining the fine-grained spatial association rules w.r.t the coarse-grained frequently patterns obtained in the first phase. Adaptive two-phase spatial association rules mining method conducts the idea of partition on an image for efficiently quantizing out non-frequent patterns and thus facilitate the following two phase process. Such two-phase approaches save much computations and will be shown by lots of experimental results in the paper.
Idioma
inglés
OAI Identifier
oai:sedici.unlp.edu.ar:10915/9516
Enlace del recurso
http://sedici.unlp.edu.ar/handle/10915/9516
http://hdl.handle.net/10915/9516
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr06-6.pdf
Nivel de acceso
Acceso abierto
Materia
Ciencias Informáticas
remote sensed image
Data mining
Spatial databases and GIS