ABSTRACT
General purpose data mining tools like Clementine from SPSS, Enterprise Miner from SAS, data mining extensions from relational database vendors such as Oracle and IBM, public domain data mining packages such as Weka, See5/C5.0 are designed for the purpose of analyzing transactional data. Although these tools were primarily designed to identify customer-buying patterns in market-basket data, they have also been used in analyzing scientific and engineering data, astronomical data, multimedia data, genomic data, and Web data. However, extracting interesting and useful patterns from spatial datasets is more difficult than extracting corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation.
