Spatial Data Preparation for Knowledge Discovery

There is a well known necessity to extract knowledge from spatial databases. Dozens of algorithms for data mining and knowledge discovery are reported in the specific literature to supply this necessity. However, these algorithms have some general drawbacks. Some consider only spatial data and others, only non-spatial data. Most are pseudo-codes, which are usually not implemented in toolkits, and need inputs in a specific representation. These inputs are often in unusual formats, and if these formats are easily obtainable in real databases is normally not mentioned nor considered. Therefore, outside academic institutions, data mining practice in real spatial databases is rare. This research proposes a methodology to prepare spatial data for data mining, as well as an interoperable software architecture to implement the methodology. Experiments with different input data formats were achieved with different data mining toolkits in order to validate the methodology. The software architecture was implemented in a data preparation toolkit and applied to artificial and real spatial databases.