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Uncertainty Handling and Quality Assessment in Data Mining

  • Michalis Vazirgiannis
  • Maria Halkidi
  • Dimitrios Gunopulos

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos
    Pages 1-9
  3. Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos
    Pages 11-71
  4. Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos
    Pages 73-127
  5. Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos
    Pages 129-181
  6. Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos
    Pages 183-198
  7. Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos
    Pages 199-221
  8. Back Matter
    Pages 223-226

About this book

Introduction

The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.

Keywords

Cluster Validity Data Mining Knowledge Discovery Quality Assessment Uncertainty Handling algorithms database

Authors and affiliations

  • Michalis Vazirgiannis
    • 1
  • Maria Halkidi
    • 1
  • Dimitrios Gunopulos
    • 2
  1. 1.Department of InformaticsAthens University of Economics and BusinessGreece
  2. 2.Department of Computer Science and EngineeringUniversity of CaliforniaRiversideUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-0031-7
  • Copyright Information Springer-Verlag London 2003
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4471-1119-1
  • Online ISBN 978-1-4471-0031-7
  • Series Print ISSN 1610-3947
  • Buy this book on publisher's site