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Data Mining and Computational Intelligence

  • Book
  • © 2001

Overview

Editors:
  1. Abraham Kandel

    1. Computer Science and Engineering, University of South Florida, Tampa, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Mark Last

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

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    You can also search for this editor in PubMed   Google Scholar

  3. Horst Bunke

    1. Department of Computer Science, University of Bern, Bern, Switzerland

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    You can also search for this editor in PubMed   Google Scholar

  • Comprehensive coverage of recent advances in the application of soft computing and fuzzy logic data mining
  • Also useful as a reference book in data mining, machine learning, fuzzy logic, and artificial intelligence

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 68)

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Table of contents (13 chapters)

  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
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Keywords

About this book

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").

Editors and Affiliations

  • 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

Bibliographic Information

  • Book Title: Data Mining and Computational Intelligence

  • Editors: Abraham Kandel, Mark Last, Horst Bunke

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-7908-1825-3

  • Publisher: Physica Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Physica-Verlag Heidelberg 2001

  • Hardcover ISBN: 978-3-7908-1371-5Published: 13 March 2001

  • Softcover ISBN: 978-3-7908-2484-1Published: 21 October 2010

  • eBook ISBN: 978-3-7908-1825-3Published: 11 November 2013

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: XII, 356

  • Topics: Database Management, IT in Business, Artificial Intelligence

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