Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Statistics: principles and methods
Statistics: principles and methods
Data mining
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade
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This chapter gives an overview of the knowledge discovery process. The full process starts from the definition and analysis of the business problem, followed by understanding and preparation of data, setup of the search for knowledge, the actual search, application of results in solving the business problem, and, finally, deployment and practical evaluation of the solutions. We outline the main tasks and methods that apply in each phase and we make references to the relevant chapters of this handbook. The discovery process is a combination of human involvement and autonomous methods of discovery. Autonomous methods may include automated task integration, for instance, integration of variable selection, knowledge mining, and result optimization. We also emphasize the iterative character of discovery including feedback loops and knowledge refinement.