Uncertainty handling and quality assessment in data mining

摘要: Uncertainty Handling and Quality Assessment in Data Mining provides an introduction to the application of these concepts Knowledge Discovery Mining. It reviews state-of-the-art uncertainty handling discusses a framework for unveiling uncertainty. Coverage quality assessment begins with cluster analysis comparison methods approaches that may be used. The techniques algorithms involved other essential data mining tasks, such as classification extraction association rules, are also discussed together review criteria evaluating results. This book presents general assessing which is based on tested theories. forms basis implementation tool, 'Uminer' introduced reader first time. tool supports key tasks while enhancing traditional processes quality. Aimed at IT professionals knowledge discovery, work supported case studies from epidemiology telecommunications illustrate how works 'real world' projects. would interest final year undergraduates or post-graduate students looking at: databases, algorithms, artificial intelligence information systems particularly regard assessment.