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Knowledge Discovery for Business Information Systems

  • Book
  • © 2002

Overview

Editors:
  1. Witold Abramowicz

    1. The Poznań University of Economics, Poland

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    You can also search for this editor in PubMed   Google Scholar

  2. Jozef Zurada

    1. University of Louisville, USA

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    You can also search for this editor in PubMed   Google Scholar

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

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Information Filters Supplying Data Warehouses with Benchmarking Information

    • Witold Abramowicz, Paweł Jan Kalczyński, Krzysztof Węcel
    Pages 1-28

  3. Parallel Mining of Association Rules

    • David Cheung, Sau Dan Lee
    Pages 29-66

  4. Unsupervised Feature Ranking and Selection

    • Manoranjan Dash, Huan Liu, Jun Yao
    Pages 67-87

  5. Approaches to Concept Based Exploration of Information Resources

    • Hele-Mai Haav, Jørgen Fischer Nilsson
    Pages 89-110

  6. Hybrid Methodology of Knowledge Discovery for Business Information

    • Zdzisław S. Hippe
    Pages 111-127

  7. Fuzzy Linguistic Summaries of Databases for an Efficient Business Data Analysis and Decision Support

    • Janusz Kacprzyk, Ronald R. Yager, Slawomir Zadrozny
    Pages 129-152

  8. Integrating Data Sources Using a Standardized Global Dictionary

    • Ramon Lawrence, Ken Barker
    Pages 153-172

  9. Maintenance of Discovered Association Rules

    • Sau Dan Lee, David Cheung
    Pages 173-209

  10. Multidimensional Business Process Analysis with the Process Warehouse

    • Beate List, Josef Schiefer, A Min Tjoa, Gerald Quirchmayr
    Pages 211-227

  11. Amalgamation of Statistics and Data Mining Techniques: Explorations in Customer Lifetime Value Modeling

    • D. R. Mani, James Drew, Andrew Betz, Piew Datta
    Pages 229-250

  12. Robust Business Intelligence Solutions

    • Jan Mrazek
    Pages 251-273

  13. The Role of Granular Information in Knowledge Discovery in Databases

    • Witold Pedrycz
    Pages 275-305

  14. Dealing with Dimensions in Data Warehousing

    • Jaroslav Pokorny
    Pages 307-324

  15. Enhancing the KDD Process in the Relational Database Mining Framework by Quantitative Evaluation of Association Rules

    • Giuseppe Psaila
    Pages 325-350

  16. Speeding up Hypothesis Development

    • Jörg A. Schlösser, Peter C. Lockemann, Matthias Gimbel
    Pages 351-375

  17. Sequence Mining in Dynamic and Interactive Environments

    • Srinivasan Parthasarathy, Mohammed J. Zaki, Mitsunori Ogihara, Sandhya Dwarkadas
    Pages 377-396

  18. Investigation of Artificial Neural Networks for Classifying Levels of Financial Distress of Firms: The Case of an Unbalanced Training Sample

    • Jozef Zurada, Benjamin P. Foster, Terry J. Ward
    Pages 397-424

  19. Back Matter

    Pages 425-431
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Keywords

About this book

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.

Editors and Affiliations

  • The Poznań University of Economics, Poland

    Witold Abramowicz

  • University of Louisville, USA

    Jozef Zurada

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