Reliable Knowledge Discovery

  • Honghua Dai
  • James N. K. Liu
  • Evgueni Smirnov

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Reliability Estimation

    1. Front Matter
      Pages 1-1
    2. Pedro Pereira Rodrigues, Zoran Bosnić, João Gama, Igor Kononenko
      Pages 29-49
    3. Nikolay Nikolaev, Evgueni Smirnov
      Pages 51-66
  3. Reliable Knowledge Discovery Methods

    1. Front Matter
      Pages 67-67
    2. Honghua Dai
      Pages 93-107
    3. Evgueni Smirnov, Georgi Nalbantov, Ida Sprinkhuizen-Kuyper
      Pages 109-126
    4. Gengxin Miao, Louise E. Moser, Xifeng Yan, Shu Tao, Yi Chen, Nikos Anerousis
      Pages 127-147
    5. Shui Yu, Simon James, Yonghong Tian, Wanchun Dou
      Pages 149-159
    6. Yan-xing Hu, James N. K. Liu, Li-wei Jia
      Pages 161-182
    7. Ida Sprinkhuizen-Kuyper, Louis Vuurpijl, Youri van Pinxteren
      Pages 183-200
  4. Reliability Analysis

    1. Front Matter
      Pages 201-201
    2. Hai Thanh Nguyen, Katrin Franke, Slobodan Petrović
      Pages 203-218
    3. Georgi Nalbantov, Patrick Groenen, Evgueni Smirnov
      Pages 227-238
  5. Reliability Improvement Methods

    1. Front Matter
      Pages 257-257
    2. James N. K. Liu, Ke Wang, Yu-Lin He, Xi-Zhao Wang
      Pages 269-290
  6. Back Matter
    Pages 307-308

About these proceedings


Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military.

Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters.

Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.


Reliable knowledge discovery Reliable system platform applications assessing knowledge reliability confidence prediction reliable classification reliable feature selection reliable graph discovery reliable regression reliable stream mining reliable web mining robust knowledge-discovery process

Editors and affiliations

  • Honghua Dai
    • 1
  • James N. K. Liu
    • 2
  • Evgueni Smirnov
    • 3
  1. 1., School of Information TechnologyDeakin UniversityBurwoodAustralia
  2. 2., ComputingHong Kong Polytechnic UniversityHunghomHong Kong SAR
  3. 3., Department of Knowledge EngineeringMaastricht UniversityMaastrichtNetherlands

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media, LLC 2012
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-1902-0
  • Online ISBN 978-1-4614-1903-7
  • Buy this book on publisher's site