Uncertainty Handling and Quality Assessment in Data Mining

Michalis Vazirgiannis|Maria Halkidi|Dimitrious Gunopulos

ISBN 10: 1852336552 / ISBN 13: 9781852336554

Edité par Springer London, 2003

A propos de cet article

Focuses on the quality assessment of the results and the use of uncertainty in data mining rather than providing a general treatment of the subject of data miningThe recent explosive growth of our ability to generate and store data has created a need fo.

N° de réf. du vendeur 4289694

Détails bibliographiques

• Titre: Uncertainty Handling and Quality Assessment in Data Mining
• Éditeur: Springer London
• Date de parution: 2003
• Langue: anglais
• ISBN à 10 chiffres: 1852336552
• ISBN à 13 chiffres: 9781852336554
• Reliure: Kartoniert / Broschiert
• État de l'article: New

A propos de ce titre

Synopsis

Covers the key topics of assessing the quality of the results and handling uncertainty in data mining.

Présentation de l'éditeur

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