Uncertainty Handling and Quality Assessment in Data Mining
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Author
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
- Internet Archiveuncertaintyhandl00phdm
- ISBN-10144710031X
- ISBN-139781447100317
- OCLC Control Number840276854
- Better World Books9781447100317
and 1 more
- Open LibraryOL27093360M
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 Mining
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