Authors:
- A new research direction of fuzzy set theory in data mining
- One of the first monographs of studying the transparency of data mining models
- Contains more than 60 figures and illustrations in order to explain complicated concepts
Part of the book series: Advanced Topics in Science and Technology in China (ATSTC)
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Table of contents (12 chapters)
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Front Matter
About this book
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.
Authors and Affiliations
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Intelligent Computing and Machine Learning Lab, School of ASEE, Beihang University, Beijing, China
Zengchang Qin
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College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, China
Yongchuan Tang
Bibliographic Information
Book Title: Uncertainty Modeling for Data Mining
Book Subtitle: A Label Semantics Approach
Authors: Zengchang Qin, Yongchuan Tang
Series Title: Advanced Topics in Science and Technology in China
DOI: https://doi.org/10.1007/978-3-642-41251-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Berlin Heidelberg 2014
Hardcover ISBN: 978-3-642-41250-9Published: 07 March 2014
eBook ISBN: 978-3-642-41251-6Published: 30 October 2014
Series ISSN: 1995-6819
Series E-ISSN: 1995-6827
Edition Number: 1
Number of Pages: XIX, 291
Additional Information: Jointly published with Zhejiang University Press, Hangzhou, China
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence, Information Systems and Communication Service, Math Applications in Computer Science