The computerization of many business and government transactions and the advances in scientific data collection tools provide us with a huge and continuously increasing amount of data. This explosive growth of databases has far outpaced the human ability to interpret this data, creating an urgent need for new techniques and tools that support the human in transforming the data into useful information and knowledge. Knowledge discovery in databases (KDD) has been defined as the non-trivial process of discovering valid, novel, and potentially useful, and ultimately understandable patterns from data [FPS 96]. The process of KDD is interactive and iterative, involving several steps such as the following ones:
• Selection: selecting a subset of all attributes and a subset of all data from which the knowledge should be discovered.
Data reduction: using dimensionality reduction or transformation techniques to reduce the effective number of attributes to be considered.
• Data mining: the application of appropriate algorithms that, under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data.
• Evaluation: interpreting and evaluating the discovered patterns with respect to their usefulness in the given application.
Spatial Database Systems (SDBS) (see [Gue 94] for an overview) are database systems for the management of spatial data. To find implicit regularities, rules or patterns hidden in large spatial databases, e.g. for geo-marketing, traffic control or environmental studies, spatial data mining algorithms are very important (see [KHA 96] for an overview of spatial data mining). 数据挖掘研究院
