Knowledge Discovery in Spatial Databases

Both, the number and the size of spatial databases, such as geographic or medical databases, are rapidly growing because of the large amount of data obtained from satellite images, computer tomography or other scientific equipment. Knowledge discovery in databases (KDD) is the process of discovering valid, novel and potentially useful patterns from large databases. Typical tasks for knowledge discovery in spatial databases include clustering, characterization and trend detection. The major difference between knowledge discovery in relational databases and in spatial databases is that attributes of the neighbors of some object of interest may have an influence on the object itself. Therefore, spatial knowledge discovery algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Thus, providing general concepts for neighborhood relations as well as an efficient impleme...

• Type: Conference
• Year: 1999
• Where: DAGM
• Authors: Martin Ester, Hans-Peter Kriegel, Jörg Sander
• Keywords: DAGM 1999, Image Processing, Knowledge Discovery, Spatial Databases, Spatial Knowledge Discovery