Data Mining for Automated GIS Data Collection

The automatic analysis of spatial data sets presumes to have techniques for interpretation and structure recognition. Such procedures are especially needed in GIS and digital cartography in order to automate the time-consuming data update and to generate multi-scale representations of the data. In order to infer higher level information from a more detailed data set, coherent, homogeneous structures in a data set have to be delineated. There are different approaches to tackle this problem, e.g. model based interpretation, rule based aggregation or clustering procedures, which are part of the main topic called data mining. In the paper, a short introduction to data mining will be given and a parameter-free graph-based clustering approach is presented