Uncertainty Modeling for Data Mining von Zengchang Qin | A Label Semantics Approach | ISBN 9783642412509

Uncertainty Modeling for Data Mining

A Label Semantics Approach

von Zengchang Qin und Yongchuan Tang
Mitwirkende
Autor / AutorinZengchang Qin
Autor / AutorinYongchuan Tang
Buchcover Uncertainty Modeling for Data Mining | Zengchang Qin | EAN 9783642412509 | ISBN 3-642-41250-5 | ISBN 978-3-642-41250-9

Uncertainty Modeling for Data Mining

A Label Semantics Approach

von Zengchang Qin und Yongchuan Tang
Mitwirkende
Autor / AutorinZengchang Qin
Autor / AutorinYongchuan Tang

Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.

Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.