Contributions

  • Halkidi, Maria - Contributor
  • Gunopulos, Dimitrios - Contributor

Publication

2003 - Springer London, London, England

Language

English

Word Count

56,500 words, Guess

Page Count

226 pages

Physical Format

Electronic resource

Identifiers

and 1 more

Classifications

  • LCCQA75.5-76.95

Description

<STRONG>Uncertainty Handling and Quality Assessment in Data Mining</STRONG> 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.

Subjects

Series Statement

  • Advanced Information and Knowledge Processing, 1610-3947
  • Advanced information and knowledge processing

Links

Other Editions

  • Uncertainty Handling and Quality Assessment in Data MiningElectronic resourceSpringer London2003-01-01

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