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Knowledge Discovery for Business Information Systems

  • Witold Abramowicz
  • Jozef Zurada

Part of the The International Series in Engineering and Computer Science book series (SECS, volume 600)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Witold Abramowicz, Paweł Jan Kalczyński, Krzysztof Węcel
    Pages 1-28
  3. David Cheung, Sau Dan Lee
    Pages 29-66
  4. Manoranjan Dash, Huan Liu, Jun Yao
    Pages 67-87
  5. Hele-Mai Haav, Jørgen Fischer Nilsson
    Pages 89-110
  6. Janusz Kacprzyk, Ronald R. Yager, Slawomir Zadrozny
    Pages 129-152
  7. Ramon Lawrence, Ken Barker
    Pages 153-172
  8. Sau Dan Lee, David Cheung
    Pages 173-209
  9. Beate List, Josef Schiefer, A Min Tjoa, Gerald Quirchmayr
    Pages 211-227
  10. Jan Mrazek
    Pages 251-273
  11. Jaroslav Pokorny
    Pages 307-324
  12. Jörg A. Schlösser, Peter C. Lockemann, Matthias Gimbel
    Pages 351-375
  13. Srinivasan Parthasarathy, Mohammed J. Zaki, Mitsunori Ogihara, Sandhya Dwarkadas
    Pages 377-396
  14. Back Matter
    Pages 425-431

About this book

Introduction

Current database technology and computer hardware allow us to gather, store, access, and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition, the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless, our ability to discover critical, non-obvious nuggets of useful information in data that could influence or help in the decision making process, is still limited.
Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory, machine learning, pattern recognition, statistics, artificial intelligence, uncertainty management, and high-performance computing.
To remain competitive, businesses must apply data mining techniques such as classification, prediction, and clustering using tools such as neural networks, fuzzy logic, and decision trees to facilitate making strategic decisions on a daily basis.
Knowledge Discovery for Business Information Systems contains a collection of 16 high quality articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Bulgaria, Canada, China (Hong Kong), Estonia, Denmark, Germany, Italy, Poland, Singapore and USA.

Keywords

Analysis Information System business process classification database hardware knowledge discovery linear optimization machine learning modeling performance relational database service-oriented computing uncertainty warehousing

Editors and affiliations

  • Witold Abramowicz
    • 1
  • Jozef Zurada
    • 2
  1. 1.The Poznań University of EconomicsPoland
  2. 2.University of LouisvilleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b116447
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-7923-7243-1
  • Online ISBN 978-0-306-46991-6
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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