Advertisement

Uncertainty Modeling for Data Mining

A Label Semantics Approach

  • Zengchang Qin
  • Yongchuan Tang

Part of the Advanced Topics in Science and Technology in China book series (ATSTC)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Zengchang Qin, Yongchuan Tang
    Pages 1-12
  3. Zengchang Qin, Yongchuan Tang
    Pages 13-38
  4. Zengchang Qin, Yongchuan Tang
    Pages 39-75
  5. Zengchang Qin, Yongchuan Tang
    Pages 77-119
  6. Zengchang Qin, Yongchuan Tang
    Pages 121-154
  7. Zengchang Qin, Yongchuan Tang
    Pages 155-176
  8. Zengchang Qin, Yongchuan Tang
    Pages 177-192
  9. Zengchang Qin, Yongchuan Tang
    Pages 193-214
  10. Zengchang Qin, Yongchuan Tang
    Pages 215-233
  11. Zengchang Qin, Yongchuan Tang
    Pages 235-252
  12. Zengchang Qin, Yongchuan Tang
    Pages 253-275
  13. Zengchang Qin, Yongchuan Tang
    Pages 277-291

About this book

Introduction

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.

Keywords

Computational Intelligence Computational Intelligence Data Mining Data Mining Fuzzy Logic Fuzzy Logic HEP HEP Intelligent Systems Intelligent Systems Modeling with Uncertainties Modeling with Uncertainties

Authors and affiliations

  • Zengchang Qin
    • 1
  • Yongchuan Tang
    • 2
  1. 1.Intelligent Computing and Machine Learning Lab, School of ASEEBeihang UniversityBeijingChina
  2. 2.College of Computer ScienceZhejiang UniversityHangzhou, ZhejiangChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-41251-6
  • Copyright Information Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-41250-9
  • Online ISBN 978-3-642-41251-6
  • Series Print ISSN 1995-6819
  • Series Online ISSN 1995-6827
  • Buy this book on publisher's site