Uncertainty Handling and Quality Assessment in Data Mining
By: by Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos
Contributors: Halkidi, Maria; Gunopulos, Dimitrios
Series: Advanced Information and Knowledge Processing, 1610-3947; Advanced information and knowledge processing
Publisher: Springer London; Imprint; Springer
Published: 2003
Place of publication: London
Language: eng
Format: [electronic resource] /
Number of pages: 226
ISBN-13: 9781447100317
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.
Table of contents: title: Introduction; title: Data Mining Process; title: Quality Assessment in Data Mining; title: Uncertainty Handling in Data Mining; title: UMINER: A Data Mining System Handling Uncertainty and Quality; title: Case Studies; title: Index.