Algorithms and applications for spatial data mining
Due to the computerization and the advances in scientific data collection we are faced with a large and continuously growing amount of data which makes it impossible to interpret all of it manually. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Knowledge Discovery in Databases (KDD) has been defined as the non-trivial process of discovering valid, novel, potentially useful and ultimately understandable patterns from data; a pattern is an expression in some language describing a subset of the data or a model applicable to that subset (Fayyad et al. 1996). The process of KDD is interactive and iterative, involving several steps such as data selection, data reduction, data mining, and the evaluation of the data mining results. The heart of the process, however, is the data mining step which consists of the application of data analysis and discovery algorithms that, under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data (Fayyad et al. 1996).