Uncertainty Modeling for Data Mining
A Label Semantics Approach
Authors: Qin, Zengchang, Tang, Yongchuan
Free Preview- 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
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- About this book
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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.
- Table of contents (12 chapters)
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Introduction
Pages 1-12
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Induction and Learning
Pages 13-38
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Label Semantics Theory
Pages 39-75
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Linguistic Decision Trees for Classification
Pages 77-119
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Linguistic Decision Trees for Prediction
Pages 121-154
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Table of contents (12 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Uncertainty Modeling for Data Mining
- Book Subtitle
- A Label Semantics Approach
- Authors
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- Zengchang Qin
- Yongchuan Tang
- Series Title
- Advanced Topics in Science and Technology in China
- Copyright
- 2014
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Berlin Heidelberg
- Distribution Rights
- Distribution rights for China: Zhejiang University Press, Hangzhou, China
- eBook ISBN
- 978-3-642-41251-6
- DOI
- 10.1007/978-3-642-41251-6
- Hardcover ISBN
- 978-3-642-41250-9
- Series ISSN
- 1995-6819
- Edition Number
- 1
- Number of Pages
- XIX, 291
- Additional Information
- Jointly published with Zhejiang University Press, Hangzhou, China
- Topics
