The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
A framework for knowledge-based temporal abstraction
Artificial Intelligence
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Social Science Computer Review - Special issue on survey and statistical computing in the new millennium
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Towards an ontology-based spatial clustering framework
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
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The volume of data being produced for administrative purposes is increasing rapidly. Data must be analysed in order to extract useful information to support decision making. The demand for evidence-based information means that the analysis must be conducted according to the principles of scientific research. Unfortunately, the massive second-hand data sets seem not to fit very well into the traditional methodological paradigm. A secondary data source imposes limitations on the formulation of a problem and concepts, because the measurement can only be based on existing data. The aim of this paper is to present a methodological framework for the utilisation of administrative registers in the creation of scientifically valid information. This is done by discussing fruitful methodological aspects encountered in the practical knowledge-discovery process. The ideas presented originate from many different fields, such as statistics, data mining and sociology. The emphasis lies on understanding connections between problem, data and analysis in the case of massive secondary administrative data sources.