Knowledge Discovery for Business Information Systems, 2001 The Springer International Series in Engineering and Computer Science Series, Vol. 600

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.

Preface. Foreword. List of Contributors. 1. Information Filters Supplying Data Warehouses with Benchmarking Information; W. Abramowicz, et al. 2. Parallel Mining of Association Rules; D. Cheung, Sau Dan Lee. 3. Unsupervised Feature Ranking and Selection; M. Dash, et al. 4. Approaches to Concept Based Exploration of Information Resources; H.-M. Haav, J.F. Nilsson. 5. Hybrid Methodology of Knowledge Discovery for Business Information; Z.S. Hippe. 6. Fuzzy Linguistic Summaries of Databases for an Efficient Business Data Analysis and Decision Support; J. Kacprzyk, et al. 7. Integrating Data Sources Using a Standardized Global Dictionary; R. Lawrence, K. Barker. 8. Maintenance of Discovered Association Rules; Sau Dan Lee, D. Cheung. 9. Multidimensional Business Process Analysis with the Process Warehouse; B. List, et al. 10. Amalgamation of Statistics and Data Mining Techniques: Explorations in Customer Lifetime Value Modeling; D.R. Mani, et al. 11. Robust Business Intelligence Solutions; J. Mrazek. 12. The Role of Granular Information in Knowledge Discovery in Databases; W. Pedrycz. 13. Dealing with Dimensions in Data Warehousing; J. Pokorny. 14. Enhancing the KDD Process in the Relational Database Mining Framework by Quantitative Evaluation of Association Rules; G. Psaila. 15. Speeding up Hypothesis Development; J.A. Schlösser, et al. 16. Sequence Mining in Dynamic and Interactive Environments; S. Parthasarathy, et al. 17. Investigation of Artificial Neural Networks forClassifying Levels of Financial Distress of Firms: The Case of an Unbalanced Training Sample; J. Zurada, et al. Index.