Advanced Topics in Science and Technology in China

Uncertainty Modeling for Data Mining

A Label Semantics Approach

Authors: Qin, Zengchang, Tang, Yongchuan

  • 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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eBook $109.00
price for USA (gross)
  • ISBN 978-3-642-41251-6
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.00
price for USA
  • ISBN 978-3-642-41250-9
  • Free shipping for individuals worldwide
  • Online orders shipping within 2-3 days.
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.

Table of contents (12 chapters)

  • Introduction

    Qin, Zengchang (et al.)

    Pages 1-12

  • Induction and Learning

    Qin, Zengchang (et al.)

    Pages 13-38

  • Label Semantics Theory

    Qin, Zengchang (et al.)

    Pages 39-75

  • Linguistic Decision Trees for Classification

    Qin, Zengchang (et al.)

    Pages 77-119

  • Linguistic Decision Trees for Prediction

    Qin, Zengchang (et al.)

    Pages 121-154

Buy this book

eBook $109.00
price for USA (gross)
  • ISBN 978-3-642-41251-6
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.00
price for USA
  • ISBN 978-3-642-41250-9
  • Free shipping for individuals worldwide
  • Online orders shipping within 2-3 days.
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Bibliographic Information

Bibliographic Information
Book Title
Uncertainty Modeling for Data Mining
Book Subtitle
A Label Semantics Approach
Authors
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