Knowledge Discovery for Business Information Systems

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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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.

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

• The Poznań University of Economics, Poland

  Witold Abramowicz

• University of Louisville, USA

  Jozef Zurada

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