Data Mining and Knowledge Discovery via Logic-Based Methods

Theory, Algorithms, and Applications

  • Evangelos¬†Triantaphyllou

Part of the Springer Optimization and Its Applications book series (SOIA, volume 43)

Table of contents

  1. Front Matter
    Pages i-xxxiii
  2. Algorithmic Issues

    1. Front Matter
      Pages 1-1
    2. Evangelos Triantaphyllou
      Pages 3-20
    3. Evangelos Triantaphyllou
      Pages 101-123
    4. Evangelos Triantaphyllou
      Pages 151-170
  3. Application Issues

    1. Front Matter
      Pages 171-171
    2. Evangelos Triantaphyllou
      Pages 229-239
    3. Evangelos Triantaphyllou
      Pages 241-255
    4. Evangelos Triantaphyllou
      Pages 257-276
    5. Evangelos Triantaphyllou
      Pages 277-287
    6. Evangelos Triantaphyllou
      Pages 309-315
  4. Back Matter
    Pages 317-350

About this book


The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human activity and interest. While numerous methods have been developed, the focus of this book presents algorithms and applications using one popular method that has been formulated in terms of binary attributes, i.e., by Boolean functions defined on several attributes that are easily transformed into rules that can express new knowledge.

This book presents methods that deal with key data mining and knowledge discovery issues in an intuitive manner, in a natural sequence, and in a way that can be easily understood and interpreted by a wide array of experts and end users. The presentation provides a unique perspective into the essence of some fundamental DM issues, many of which come from important real life applications such as breast cancer diagnosis.

Applications and algorithms are accompanied by extensive experimental results and are presented in a way such that anyone with a minimum background in mathematics and computer science can benefit from the exposition. Rigor in mathematics and algorithmic development is not compromised and each chapter systematically offers some possible extensions for future research.


Artifical Intelligence Boolean function Data Analysis Decision Making Intelligent Systems Knowledge Discovery Learning Systems algorithms data mining logic mathematical logic

Authors and affiliations

  • Evangelos¬†Triantaphyllou
    • 1
  1. 1., Department of Computer ScienceLouisiana State UniversityBaton RougeUSA

Bibliographic information

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