Data Mining and Computational Intelligence

1. Front Matter

   Pages I-XII

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2. Data Mining with Neuro-Fuzzy Models

   • A. Klose, A. Nürnberger, D. Nauck, R. Kruse

   Pages 1-35

3. Granular Computing in Data Mining

   • Witold Pedrycz

   Pages 37-61

4. Fuzzification and Reduction of Information-Theoretic Rule Sets

   • Mark Last, Abraham Kandel

   Pages 63-93

5. Mining Fuzzy Association Rules in a Database Containing Relational and Transactional Data

   • Keith C. C. Chan, Wai-Ho Au

   Pages 95-114

6. Fuzzy Linguistic Summaries via Association Rules

   • Janusz Kacprzyk, Slawomir Zadrożny

   Pages 115-139

7. The Fuzzy-ROSA Method: A Statistically Motivated Fuzzy Approach for Data-Based Generation of Small Interpretable Rule Bases in High-Dimensional Search Spaces

   • T. Slawinski, A. Krone, P. Krause, H. Kiendl

   Pages 141-166

8. Discovering Knowledge from Fuzzy Concept Lattice

   • Sadok Ben Yahia, Ali Jaoua

   Pages 167-190

9. Mining of Labeled Incomplete Data Using Fast Dimension Partitioning

   • Bill C. H. Chang, Saman K. Halgamuge

   Pages 191-216

10. Mining a Growing Feature Map by Data Skeleton Modelling

    • D. Alahakoon, S. K. Halgamuge, B. Srinivasan

    Pages 217-250

11. Soft Regression — A Data Mining Tool

    • E. Shnaider, M. Schneider

    Pages 251-272

12. Some Practical Applications of Soft Computing and Data Mining

    • Hung T. Nguyen, Nadipuram R. Prasad, Vladik Kreinovich, Habib Gassoumi

    Pages 273-307

13. Intelligent Mining in Image Databases, with Applications to Satellite Imaging and to Web Search

    • Stephen Gibson, Vladik Kreinovich, Luc Longpre, Brian Penn, Scott A. Starks

    Pages 309-336

14. Fuzzy Genetic Modeling and Forecasting for Nonlinear Time Series

    • Berlin Wu

    Pages 337-356

Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable").

• computational intelligence
• data mining
• database
• database management
• fuzzy
• fuzzy logic
• intelligence
• knowledge
• knowledge discovery
• knowledge discovery in databases
• learning
• linguistics
• machine learning
• modeling
• service-oriented computing

• Computer Science and Engineering, University of South Florida, Tampa, USA

  Abraham Kandel

• Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

  Mark Last

• Department of Computer Science, University of Bern, Bern, Switzerland

  Horst Bunke

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