Uncertainty handling and quality assessment in data mining

Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos

234 pages, parution le 07/08/2003

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Résumé

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.

Introduction
Data Mining Process
Quality Assessment in Data Mining
Uncertainty Handling in Data Mining
UMINER: A Data Mining System Handling Uncertainty and Quality
Case Studies

Index

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos
Parution	07/08/2003
Nb. de pages	234
Format	16 x 24
Couverture	Relié
Poids	470g
Intérieur	Noir et Blanc
EAN13	9781852336554
ISBN13	978-1-85233-655-4